Visual object tracking—classical and contemporary approaches

Ahmad ALI, Abdul JALIL, Jianwei NIU, Xiaoke ZHAO, Saima RATHORE, Javed AHMED, Muhammad AKSAM IFTIKHAR

PDF(768 KB)
PDF(768 KB)
Front. Comput. Sci. ›› 2016, Vol. 10 ›› Issue (1) : 167-188. DOI: 10.1007/s11704-015-4246-3
REVIEW ARTICLE

Visual object tracking—classical and contemporary approaches

Author information +
History +

Abstract

Visual object tracking (VOT) is an important subfield of computer vision. It has widespread application domains,and has been considered as an important part of surveillance and security system. VOA facilitates finding the position of target in image coordinates of video frames.While doing this, VOA also faces many challenges such as noise, clutter, occlusion, rapid change in object appearances, highly maneuvered (complex) object motion, illumination changes. In recent years, VOT has made significant progress due to availability of low-cost high-quality video cameras as well as fast computational resources, and many modern techniques have been proposed to handle the challenges faced by VOT.This article introduces the readers to 1) VOT and its applications in other domains, 2) different issues which arise in it, 3) various classical as well as contemporary approaches for object tracking, 4) evaluation methodologies for VOT, and 5) online resources, i.e., annotated datasets and source code available for various tracking techniques.

Keywords

visual object tracking / computer vision / image processing / point tracking / kernel tracking / silhouette tracking

Cite this article

Download citation ▾
Ahmad ALI, Abdul JALIL, Jianwei NIU, Xiaoke ZHAO, Saima RATHORE, Javed AHMED, Muhammad AKSAM IFTIKHAR. Visual object tracking—classical and contemporary approaches. Front. Comput. Sci., 2016, 10(1): 167‒188 https://doi.org/10.1007/s11704-015-4246-3

References

[1]
Ta D N, Chen WC, Gelfand N, Pulli K.Surftrac: efficient tracking and continuous object recognition using local feature descriptors. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2009, 2937–2944
[2]
Skrypnyk I, Lowe D G.Scene modelling, recognition and tracking with invariant image features. In: Proceedings of IEEE and ACM International Symposium on Mixed and Augmented Reality. 2004, 110–119
CrossRef Google scholar
[3]
Chau D P, Bremond F, Thonnat M.Object tracking in videos: approaches and issues. 2013, arXiv preprint arXiv: 1304.5212
[4]
Ko T.A survey on behavior analysis in video surveillance for homeland security applications. In: Proceedings of the 37th IEEE Applied Imagery Pattern Recognition Workshop. 2008, 1–8
CrossRef Google scholar
[5]
Ess A, Schindler K, Leibe B, Van Gool L.Object detection and tracking for autonomous navigation in dynamic environments. The International Journal of Robotics Research, 2010, 29: 1707–1725
CrossRef Google scholar
[6]
Mistry P, Maes P.SixthSense: a wearable gestural interface. In: Proceedings of ACM SIGGRAPH ASIA 2009 Sketches. 2009, 11
CrossRef Google scholar
[7]
Bradski G R.Real time face and object tracking as a component of a perceptual user interface. In: Proceedings of the 4th IEEE Workshop on Applications of Computer Vision. 1998, 214–219
CrossRef Google scholar
[8]
Zhu Z, Ji Q.Eye gaze tracking under natural head movements. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 918–923
[9]
Kim I, Choi H S, Yi K M, Choi J Y, Kong S G.Intelligent visual surveillance — a survey. International Journal of Control, Automation and Systems, 2010, 8(5): 926–939
CrossRef Google scholar
[10]
Siemens S.Sistore CX EDS-intelligent video detection system. Technical Report. 2008
[11]
Collins R, Lipton A, Kanade T, Tolliver E.A system for video surveillance and monitoring. Technical Report CMU-RI-TR-00-12. 2000
[12]
Haritaoglu I, Harwood D, Davis L S.W4: real-time surveillance of people and their activities. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 809–830
CrossRef Google scholar
[13]
Kettnaker V, Zabih R.Bayesian multi-camera surveillance. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999, 242–259
CrossRef Google scholar
[14]
Hu W, Tan T, Wang L, Maybank S.A survey on visual surveillance of object motion and behaviors. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2004, 34(3): 334–352
CrossRef Google scholar
[15]
Collins R T, Lipton A J, Fujiyoshi H, Kanade T.Algorithms for cooperative multisensor surveillance. Proceedings of the IEEE, 2001, 89(10): 1456–1477
CrossRef Google scholar
[16]
Greiffenhagen M, Comaniciu D, Niemann H, Ramesh V.Design, analysis, and engineering of video monitoring systems: an approach and a case study. Proceedings of the IEEE, 2001, 89(10): 1498–1517
CrossRef Google scholar
[17]
Kumar R, Sawhney H, Samarasekera S, Hsu S, Tao H, Guo Y, Hanna K, Pope A, Wildes R, Hirvonen D, Hansen M, Burt P.Aerial video surveillance and exploitation. Proceedings of the IEEE, 2001, 89(10):1518–1539
CrossRef Google scholar
[18]
Coifman B, Beymer D, McLauchlan P, Malik J.A real-time computer vision system for vehicle tracking and traffic surveillance. Transporta tion Research Part C: Emerging Technologies, 1998, 6(4): 271–288
CrossRef Google scholar
[19]
Tai J C, Tseng S T, Lin C P, Song K T.Real-time image tracking for automatic traffic monitoring and enforcement applications. Image and Vision Computing, 2004, 22(6): 485–501
CrossRef Google scholar
[20]
Masoud O, Papanikolopoulos N P.A novel method for tracking and counting pedestrians in real-time using a single camera. IEEE Transactions on Vehicular Technology, 2001, 50(5): 1267–1278
CrossRef Google scholar
[21]
Papanikolopoulos N P, Khosla P K.Adaptive robotic visual tracking:theory and experiments. IEEE Transactions on Automatic Control, 1993, 38(3): 429–445
CrossRef Google scholar
[22]
Sakagami Y, Watanabe R, Aoyama C, Matsunaga S, Higaki N, Fujimura K.The intelligent asimo: system overview and integration.In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. 2002, 2478–2483
CrossRef Google scholar
[23]
Mondragon I F, Campoy P, Correa J F, Mejias L.Visual model feature tracking for UAV control. In: Proceedings of IEEE International Symposium on Intelligent Signal Processing. 2007, 1–6
CrossRef Google scholar
[24]
Lee J, Huang R, Vaughn A, Xiao X, Hedrick J K, Zennaro M, Sengupta R.Strategies of path-planning for a UAV to track a ground vehicle. In:Proceedings of Annual Autonomous Intelligent Networks and Systems Conference. 2003
[25]
Handmann U, Kalinkea T, Tzomakas C, Werner M, von Seelen W.Computer vision for driver assistance systems. In: Proceedings of Aerospace/Defense Sensing and Controls. 1998, 136–147
[26]
Avidan S.Support vector tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(8): 1064–1072
CrossRef Google scholar
[27]
Ahmed J, Shah M, Miller A, Harper D, Jafri M N.A vision-based system for a UGV to handle a road intersection. In: Proceedings of National Conference on Artificial Intelligence. 2007, 1077
[28]
Rand D, Kizony R,Weiss P T.The Sony playstation II eyetoy: low-cost virtual reality for use in rehabilitation. Journal of Neurologic Physical Therapy, 2008, 32(4): 153–163
CrossRef Google scholar
[29]
Wang S, Xiong X, Xu Y, Wang C, Zhang W, Dai X, Zhang D.Facetracking as an augmented input in video games: enhancing presence, role-playing and control. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems. 2006, 1097–1106
CrossRef Google scholar
[30]
Amini A A, Owen R L, Anandan P, Duncan J.Non-rigid motion models for tracking the left ventricular wall. In: Proceedings of the 12th International Conference on Information Processing in Medical Imaging.1991, 343–357
CrossRef Google scholar
[31]
Vasconcelos M J M, Ventura S M R, Freitas D R S, Tavares J M R S.Using statistical deformable models to reconstruct vocal tract shape from magnetic resonance images. Institution ofMechanical Engineers,Part H: Journal of Engineering in Medicine, 2010, 224(10): 1153–1163
CrossRef Google scholar
[32]
Vasconcelos M J M, Ventura S M R, Freitas D R S, Tavares J M RS.Towards the automatic study of the vocal tract from magnetic resonance images. Journal of Voice: Official Journal of the Voice Foundation,2011, 25: 732–742
CrossRef Google scholar
[33]
Stauffer C, Grimson W E L.Learning patterns of activity using realtime tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 747–757
CrossRef Google scholar
[34]
Bodor R, Jackson M, Papanikolopoulos N.Vision-based human tracking and activity recognition. In: Proceedings of the 11thMediterranean Conference on Control and Automation. 2003, 18–20
[35]
Lucas B D, Kanade T.An iterative image registration technique with an application to stereo vision. In: Proceedings of International Joint Conference on Artificial Intelligence. 1981, 674–679
[36]
Fitts J M.Precision correlation tracking via optimal weighting functions.In: Proceedings of the 18th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes. 1979, 280–283
CrossRef Google scholar
[37]
Yilmaz A, Javed O, Shah M.Object tracking: a survey. ACM Computing Surveys, 2006, 38(4): 13
CrossRef Google scholar
[38]
Joshi K A, Thakore D G. A survey on moving object detection and tracking in video surveillance system. International Journal of Soft Computing and Engineering, 2012: 2231–2307
[39]
Yang H, Shao L, Zheng F, Wang L, Song Z.Recent advances and trends in visual tracking: a review. Neurocomputing, 2011, 74(18):3823–3831
CrossRef Google scholar
[40]
Cannons K.A review of visual tracking. Technical Report CSE-2008-07. 2008
[41]
Geronimo D, Lopez A M, Sappa A D, Graf T.Survey of pedestrian detection for advanced driver assistance systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(7): 1239–1258
CrossRef Google scholar
[42]
Ogale N A.A survey of techniques for human detection. Master’s Thesis. University of Maryland, 2006
[43]
Trucco E, Plakas K.Video tracking: a concise survey. IEEE Journal of Oceanic Engineering, 2006, 31(2): 520–529
CrossRef Google scholar
[44]
Moeslund T B, Hilton A, Krüger V.A survey of advances in visionbased human motion capture and analysis. Computer Vision and Image Understanding, 2006, 104(2): 90–126
CrossRef Google scholar
[45]
Aggarwal J K, Cai Q.Human motion analysis: a review. In: Proceedings of IEEE Nonrigid and Articulated Motion Workshop. 1997, 90–102
CrossRef Google scholar
[46]
Kang W, Deng F.Research on intelligent visual surveillance for public security. In: Proceedings of IEEE/ACIS International Conference on Computer and Information Science. 2007, 824–829
CrossRef Google scholar
[47]
Forsyth D A, Arikan O, Ikemoto L.Computational Studies of Human Motion: Tracking and Motion Synthesis. Boston: Now Publishers Inc.2006
[48]
Zhan B, Monekosso D N, Remagnino P, Velastin S A, Xu L Q.Crowd analysis: a survey. Machine Vision and Applications, 2008, 19(5-6):345–357
CrossRef Google scholar
[49]
Arulampalam M S, Maskell S, Gordon N, Clapp T.A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Transactions on Signal Processing, 2002, 50(2): 174–188
CrossRef Google scholar
[50]
Jalal A S, Singh J.The state-of-the-art in visual object tracking. Informatica Slovenia, 2012, 36(3): 227–248
[51]
Li X, Hu W, Shen C, Zhang Z, Dick A, Hengel A V D.A survey of appearance models in visual object tracking. ACM Transactions on Intelligent Systems and Technology, 2013, 4(4): 58
CrossRef Google scholar
[52]
Fukunaga K, Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition. IEEE Transactions on Information Theory, 1975, 21(1): 32–40
CrossRef Google scholar
[53]
Cheng Y.Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(8): 790–799
CrossRef Google scholar
[54]
Comaniciu D, Meer P.Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603–619
CrossRef Google scholar
[55]
Comaniciu D, Meer P.Robust analysis of feature spaces: color image segmentation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1997, 750–755
CrossRef Google scholar
[56]
Comaniciu D, Ramesh V, Meer P.Real-time tracking of non-rigid objects using mean shift. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2000, 142–149
CrossRef Google scholar
[57]
Comaniciu D, Ramesh V, Meer P.Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003,25(5): 564–575
CrossRef Google scholar
[58]
Hero A O, Ma B, Michel O J J, Gorman J.Applications of entropic spanning graphs. IEEE Signal Processing Magazine, 2002, 19(5): 85–95
CrossRef Google scholar
[59]
Yang C, Duraiswami R, Davis L.Efficient mean-shift tracking via a new similarity measure. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 176–183
[60]
Beleznai C, Fruhstuck B, Bischof H.Human tracking by fast mean shift mode seeking. Journal of Multimedia, 2006, 1(1): 1–8
CrossRef Google scholar
[61]
Beleznai C, Fruhstuck B, Bischof H.Human tracking by mode seeking. In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis. 2005, 1–6
CrossRef Google scholar
[62]
Beleznai C, Fruhstuck B, Bischof H.Tracking multiple humans by fast mean shift mode seeking. In: Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance. 2005,25–32
[63]
Beleznai C, Fruhstuck B, Bischof H.Detecting humans in groups using a fast mean shift procedure. In: Proceedings of Workshop of the Austrian Association for Pattern Recogniton. 2004, 71–78
[64]
Beleznai C, Fruhstuck B, Bischof H.Human detection in groups using a fast mean shift procedure. In: Proceedings of International Conference on Image Processing. 2004, 349–352
CrossRef Google scholar
[65]
Zivkovic Z, Krose B.An EM-like algorithm for color-histogram-based object tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2004, 798–803
CrossRef Google scholar
[66]
Zhou H, Yuan Y, Zhang Y, Shi C.Non-rigid object tracking in complex scenes. Pattern Recognition Letters, 2009, 30(2): 98–102
CrossRef Google scholar
[67]
Ning J, Zhang L, Zhang D, Wu C.Robust object tracking using joint color-texture histogram. International Journal of Pattern Recognition and Artificial Intelligence, 2009, 23: 1245–1263
CrossRef Google scholar
[68]
Shan C, Tan T,Wei Y.Real-time hand tracking using a mean shift embedded particle filter. Pattern Recognition, 2007, 40(7): 1958–1970
CrossRef Google scholar
[69]
Wang X, Liu L, Tang Z.Infrared human tracking with improved mean shift algorithm based on multicue fusion. Journal of Applied Otics,2009, 48(21): 4201–4212
CrossRef Google scholar
[70]
Shen C, Brooks M J, Van Den Hengel A.Fast global kernel density mode seeking: applications to localization and tracking. IEEE Transactions on Image Processing, 2007, 16(5): 1457–1469
CrossRef Google scholar
[71]
Adam A, Rivlin E, Shimshoni I.Robust fragments-based tracking using the integral histogram. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006, 798–805
CrossRef Google scholar
[72]
Jeyakar J, Babu R V, Ramakrishnan K R.Robust object tracking with background-weighted local kernels. Computer Vision and Image Understanding,2008, 112(3):296–309
CrossRef Google scholar
[73]
Khan M I, Ahmed J, Ali A, Masood A.Robust edge-enhanced fragment based normalized correlation tracking in cluttered and occluded imagery. In: Proceedings of Signal Processing, Image Processing and Pattern Recognition. 2009, 169–176
CrossRef Google scholar
[74]
Kalman R E, Bucy R S.New results in linear filtering and prediction theory. Journal of Basic Engineering, 1961, 83: 95–108
CrossRef Google scholar
[75]
Brookner E.Tracking and Kalman Filtering Made Easy. New York:Wiley, 1998
CrossRef Google scholar
[76]
Grewal MS, Andrews A P.Kalman filtering: theory and practice using MATLAB. New York, Chichester, Brisbane: JohnWiley & Sons, 2008
CrossRef Google scholar
[77]
Welch G, Bishop G.An introduction of the kalman filter. Technical Report. 2005
[78]
Asgarizadeh M, Pourghassem H.A robust object tracking synthetic structure using regional mutual information and edge correlation-based tracking algorithm in aerial surveillance application. Signal, Image and Video Processing, 2015, 9(1): 175–189
CrossRef Google scholar
[79]
Comaniciu D, Ramesh V.Mean shift and optimal prediction for efficient object tracking. In: Proceedings of International Conference on Image Processing. 2000, 70–73
CrossRef Google scholar
[80]
Li Z, Xu C, Li Y.Robust object tracking using mean shift and fast motion estimation. In: Proceedings of IEEE International Symposium on Intelligent Signal Processing and Communication Systems. 2007,734–737
[81]
Li X, Zhang T, Shen X, Sun J.Object tracking using an adaptive kalman filter combined with mean shift. Optical Engineering, 2010,49(2): 020503
CrossRef Google scholar
[82]
Ali A, Mirza S M.Object tracking using correlation, kalman filter and fast means shift algorithms. In: Proceedings of International Conference on Emerging Technologies. 2006, 174–178
CrossRef Google scholar
[83]
Ahmed J, Jafri M N, Shah M, Akbar M.Real-time edge-enhanced dynamic correlation and predictive open-loop car-following control for robust tracking. Machine Vision and Applications, 2008, 19(1): 1–25
CrossRef Google scholar
[84]
Boykov Y, Huttenlocher D P.Adaptive bayesian recognition in tracking rigid objects. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2000, 697–704
CrossRef Google scholar
[85]
Beymer D, McLauchlan P, Coifman B, Malik J.A real-time computer vision system for measuring traffic parameters. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 1997,495–501
CrossRef Google scholar
[86]
Broida T J, Chellappa R.Estimation of object motion parameters from noisy images. IEEE Transactions on Pattern Analysis and Machine Intelligence,1986, 8(1): 90–99
CrossRef Google scholar
[87]
Gennery D B.Visual tracking of known three-dimensional objects. International Journal of Computer Vision, 1992, 7(3): 243–270
CrossRef Google scholar
[88]
Terzopoulos D, Szeliski R.Tracking with kalman snakes. In: Active Vision. Cambridge, MA, USA: MIT Press, 1993, 3–20
[89]
Blake A, Isard M.Active Contours: The Application of Techniques from Graphics, Vision, Control Theory and Statistics to Visual Tracking of Shapes in Motion. 1st ed. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 1998
CrossRef Google scholar
[90]
Cuevas E V, Zaldivar D, Rojas R.Kalman filter for vision tracking.Technical Report. 2005
[91]
Jang D S, Choi H I.Active models for tracking moving objects. Pattern Recognition, 2000, 33(7): 1135–1146
CrossRef Google scholar
[92]
Ridder C, Munkelt O, Kirchner H.Adaptive background estimation and foreground detection using kalman-filtering. In: Proceedings of International Conference on recent Advances in Mechatronics. 1995,193–199
[93]
Peterfreund N.Robust tracking of position and velocity with kalman snakes. IEEE Transactions on Pattern Analysis and Machine Intelligence,1999, 21(6): 564–569
CrossRef Google scholar
[94]
Anderson B D O, Moore J B.Optimal Filtering. Mincola: Courier Dover Publications, 2012
[95]
Doucet A, Godsill S, Andrieu C.On sequential monte carlo sampling methods for bayesian filtering. Statistics and Computing, 2000, 10(3):197–208
CrossRef Google scholar
[96]
Isard M, Blake A.Condensation–conditional density propagation for visual tracking. International Journal of Computer Vision, 1998, 29(1):5–28
CrossRef Google scholar
[97]
Rao G M, Satyanarayana C.Visual object target tracking using particle filter: a survey. International Journal of Image, Graphics and Signal Processing, 2013, 5(6): 57–71
CrossRef Google scholar
[98]
Duda R O, Hart P E.Pattern Classification and Scene Analysis. New York: Wiley, 1973
[99]
Gonzalez R C, Woods R E.Digital Image Processing. Upper Saddle River, N.J.: Pearson/Prentice Hall, 2008
[100]
Kuglin C D, Hines D C.The phase correlation image alignment method. IEEE Conference on Cybernetics and Society, 1975, 163–165
[101]
Lewis J P.Fast normalized cross-correlation. Vision Interface, 1995,10(1): 120–123
[102]
Chien S I, Sung S H.Adaptive window method with sizing vectors for reliable correlation-based target tracking. Pattern Recognition, 2000,33(2): 237–249
CrossRef Google scholar
[103]
Manduchi R, Mian G A.Accuracy analysis for correlation-based image registration algorithms. In: Proceedings of IEEE International Symposium on Circuits and Systems. 1993, 834–837
[104]
Stone H S, Tao B, McGuire M.Analysis of image registration noise due to rotationally dependent aliasing. Journal of Visual Communication and Image Representation, 2003, 14(2): 114–135
CrossRef Google scholar
[105]
Stone H S.Fourier-based image registration techniques. Technical Report.2002
[106]
Foroosh H, Zerubia J B, Berthod M.Extension of phase correlation to subpixel registration. IEEE Transactions on Image Processing, 2002,11(3): 188–200
CrossRef Google scholar
[107]
Keller Y, Averbuch A, Miller O.Robust phase correlation. In: Proceedings of the 17th International Conference on Pattern Recognition.2004, 740–743
CrossRef Google scholar
[108]
Ahmed J, Jafri M N.Improved phase correlation matching. In: Proceedings of International Conference on Image and Signal Processing.2008, 128–135
CrossRef Google scholar
[109]
Blackman S S, Popoli R F.Design and Analysis of Modern Tracking Systems. Boston, M A: Artech House, 1999
[110]
Nixon M S, Aguado A S.Feature Extraction & Image Processing.London: Academic Press, 2008
[111]
Ali A, Jalil A, Ahmed J, Iftikhar M A, Hussain M.Correlation, kalman filter and adaptive fast mean shift based heuristic approach for robust visual tracking. Signal, Image and Video Processing, 2014: 1–19
[112]
Wren C R, Azarbayejani A, Darrell T, Pentland A P.Pfinder: real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 780–785
CrossRef Google scholar
[113]
Grimson W E L, Stauffer C, Romano R, Lee L.Using adaptive tracking to classify and monitor activities in a site. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.1998, 22–29
CrossRef Google scholar
[114]
Stauffer C, Grimson W E L.Adaptive background mixture models for real-time tracking. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1999
CrossRef Google scholar
[115]
KaewTraKulPong P, Bowden R.An improved adaptive background mixture model for real-time tracking with shadow detection. Video-Based Surveillance Systems. 2002, 135–144
[116]
Horprasert T, Harwood D, Davis L S.A robust background subtraction and shadow detection. In: Proceedings of Asian Conference on Computer Vision. 1999, 983–988
[117]
Horprasert T, Harwood D, Davis L S.A statistical approach for realtime robust background subtraction and shadow detection. In: Proceedings of International Conference on Computer Vision. 1999, 1–19
[118]
Oliver NM, Rosario B, Pentland A P.A bayesian computer vision system for modeling human interactions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 831–843
CrossRef Google scholar
[119]
Lipton A J, Fujiyoshi H, Patil R S.Moving target classification and tracking from real-time video. In: Proceedings of the 4th IEEE Workshop on Applications of Computer Vision. 1998, 8–14
CrossRef Google scholar
[120]
Dailey D J, Cathey FW, Pumrin S.An algorithm to estimate mean traffic speed using uncalibrated cameras. IEEE Transactions on Intelligent Transportation Systems, 2000, 1(2): 98–107
CrossRef Google scholar
[121]
Dailey D J, Li L.An algorithm to estimate vehicle speed using uncalibrated cameras. In: Proceedings of IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems. 1999, 441–446
CrossRef Google scholar
[122]
Horn B K P, Schunck B G.Determining optical flow. Technical Report.1980
[123]
Black M J, Anandan P.The robust estimation of multiple motions:Parametric and piecewise-smooth flow fields. Computer Vision and Image Understanding, 1996, 63(1): 75–104
CrossRef Google scholar
[124]
Szeliski R, Coughlan J.Spline-based image registration. International Journal of Computer Vision, 1997, 22(3): 199–218
CrossRef Google scholar
[125]
Shi J, Tomasi C.Good features to track. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.1994, 593–600
[126]
Rangarajan K, Shah M.Establishing motion correspondence. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1991, 103–108
CrossRef Google scholar
[127]
Papageorgiou C P, Oren M, Poggio T.A general framework for object detection. In: Proceedings of the 6th IEEE International Conference on Computer Vision. 1998, 555–562
CrossRef Google scholar
[128]
Cremers D, Schnorr C.Statistical shape knowledge in variational motion segmentation. Image and Vision Computing, 2003, 21(1): 77–86
CrossRef Google scholar
[129]
Li B, Chellappa R, Zheng Q, Der S Z.Model-based temporal object verification using video. IEEE Transactions on Image Processing, 2001, 10(6): 897–908
CrossRef Google scholar
[130]
Bertalmio M, Sapiro G, Randall G.Morphing active contours. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000,22(7): 733–737
CrossRef Google scholar
[131]
Mansouri A R.Region tracking via level set PDEs without motion computation. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002, 24(7):947–961
CrossRef Google scholar
[132]
Babenko B, Yang M H, Belongie S.Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(8): 1619–1632
CrossRef Google scholar
[133]
Grabner H, Grabner M, Bischof H.Real-time tracking via on-line boosting. In: Proceedings of British Machine Vision Conference.2006, 1(5): 6
CrossRef Google scholar
[134]
Collins R T, Liu Y, Leordeanu M.Online selection of discriminative tracking features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1631–1643
CrossRef Google scholar
[135]
Santner J, Leistner C, Saffari A, Pock T, Bischof H.Prost: parallel robust online simple tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 723–730
CrossRef Google scholar
[136]
Liu X, Yu T.Gradient feature selection for online boosting. In: Proceedings of the 11th IEEE International Conference on Computer Vision.2007, 1–8
CrossRef Google scholar
[137]
Avidan S.Ensemble tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 261–271
CrossRef Google scholar
[138]
Wang J, Chen X, Gao W.Online selecting discriminative tracking features using particle filter. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2005, 1037–1042
[139]
Kuncheva L I.Combining pattern classifiers: methods and algorithms. IEEE Transactions on Neural Networks, 2007, 18(3): 964–964
CrossRef Google scholar
[140]
Bishop C M.Pattern Recognition and Machine Learning. Springer,2006
[141]
Hare S, Saffari A, Torr P H S.Struck: structured output tracking with kernels. In: Proceedings of IEEE International Conference on Computer Vision. Nov 2011, 263–270
[142]
Stalder S, Grabner H.On-line Boosting Trackers. ETH-Zurich, 2009
[143]
Grabner H, Leistner C, Bischof H.Semi-supervised on-line boosting for robust tracking. In: Proceedings of European Conference on Computer Vision. 2008, 234–247
[144]
Zeisl B, Leistner C, Saffari A, Bischof H.On-line semi-supervised multiple-instance boosting. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1879–1879
[145]
Saffari A, Leistner C, Godec M, Bischof H.Robust multi-view boosting with priors. In: Proceedings of European Conference on Computer Vision, 2010, 776–789
[146]
Leistner C, Saffari A, Roth P M, Bischof H.On robustness of on-line boosting—a competitive study. In: Proceedings of IEEE International Conference on Computer Vision Workshops. 2009, 1362–1369
[147]
Masnadi-Shirazi H, Mahadevan V, Vasconcelos N.On the design of robust classifiers for computer vision. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 779–786
[148]
Zhang K, Song H.Real-time visual tracking via online weighted multiple instance learning. Pattern Recognition, 2013, 46(1): 397–411
[149]
Williams O, Blake A, Cipolla R.A sparse probabilistic learning algorithm for real-time tracking. In: Proceedings of IEEE International Conference on Computer Vision. 2003, 353–360
[150]
Kennedy J, Eberhart R.Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. 1995, 1942–1948
[151]
Eberhart R, Kennedy J.A new optimizer using particle swarm theory.In: Proceedings of the 6th International Symposium onMicroMachine and Human Science. 1995, 39–43
[152]
Poli R.Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications,2008, 2008: 3
[153]
Clerc M, Kennedy J.The particle swarm — explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 2002, 6(1): 58–73
[154]
Wachowiak M P, Smolikova R, Zheng Y, Zurada J M, Elmaghraby AS.An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 289–301
[155]
Engelbrecht A P.Computational Intelligence: an Introduction. 2nd ed.New York: John Wiley & Sons, 2007
[156]
Sedighizadeh D, Masehian E.Particle swarm optimization methods, taxonomy and applications. International Journal of Computer Theory and Engineering, 2009, 1(5): 486–502
[157]
Zhang X, Hu W, Maybank S, Zhu M.Sequential particle swarm optimization for visual tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2008, 1–8
[158]
Zhang X, Hu W, Qu W, Maybank S.Multiple object tracking via species-based particle swarm optimization. IEEE Transactions on Circuits and Systems for Video Technology, 2010, 20(11): 1590–1602
[159]
Akbari R, Jazi M D, Palhang M.A hybrid method for robust multiple objects tracking in cluttered background. In: Proceedings of the 2nd International Conference on Information & Communication Technologies. 2006, 1562–1567
[160]
Kwolek B.Multi-object tracking using particle swarm optimization on target interactions. In: Proceedings of Advances in Heuristic Signal Processing and Applications. 2013, 63–78
[161]
Anton-Canalis L, Hernandez-Tejera M, Sanchez-Nielsen E.Particle swarms as video sequence inhabitants for object tracking in computer vision. In: Proceedings of the 6th International Conference on Intelligent Systems Design and Applications. 2006, 604–609
[162]
Zheng Y, Meng Y.Adaptive object tracking using particle swarm optimization.In: Proceedings of International Symposium on Computational Intelligence in Robotics and Automation. 2007, 43–48
[163]
Tawab A M A, Abdelhalim M B, Habib S E D.Efficient multi-feature PSO for fast gray level object-tracking. Applied Soft Computing, 2014,14: 317–337
[164]
Borra S K, Chaparala S K.Tracking of an object in video stream using a hybrid PSO-FCM and pattern matching. International Journal of Engineering Research and Technology, 2013, 2
[165]
Donoho D L.Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306
[166]
Candes E J, Romberg J K, Tao T.Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207–1223
[167]
Wright J, Ma Y, Mairal J, Sapiro G, Huang T S, Yan S.Sparse representation for computer vision and pattern recognition. Proceedings of the IEEE, 2010, 98(6): 1031–1044
[168]
Sapiro G, Mairal J, Wright J, Ma Y, Huang T, Yan S.Sparse representation for computer vision and pattern recognition. Technical Report.2009
[169]
Yang J, Wright J, Huang T S, Ma Y.Image super-resolution via sparse representation. IEEE Transactions on Image Processing, 2010, 19(11):2861–2873
[170]
Wright J, Yang A Y, Ganesh A, Sastry S S, Ma Y.Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210–227
[171]
Mei X, Ling H.Robust visual tracking using l1 minimization. In: Proceedings of IEEE International Conference on Computer Vision. 2009,1436–1443
[172]
Mei X.Visual tracking and illumination recovery via sparse representation.Dissertation for the Doctoral Degree. University of Maryland,2009
[173]
Mei X, Ling H.Robust visual tracking and vehicle classification via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(11): 2259–2272
[174]
Liu B, Yang L, Huang J, Meer P, Gong L, Kulikowski C.Robust and fast collaborative tracking with two stage sparse optimization. In: Proceedings of European Conference on Computer Vision. 2010, 624–637
[175]
Liu J, Huang J, Yang L, Kulikowski C.Robust tracking using local sparse appearance model and k-selection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2011, 1313–1320
[176]
Zhong W, Lu H, Yang H M.Robust object tracking via sparsity-based collaborative model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1838–1845
[177]
Jia X, Lu X, Yang M H.Visual tracking via adaptive structural local sparse appearance model. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2012, 1822–1829
[178]
Zhang K, Zhang L, Yang M H.Real-time compressive tracking. In:Proceedings of European Conference on Computer Vision. 2012, 864–877
[179]
Zhang S, Yao H, Sun X, Lu X.Sparse coding based visual tracking: review and experimental comparison. Pattern Recognition, 2013, 46(7):1772–1788
[180]
Oliva A, Torralba A.The role of context in object recognition. Trends in Cognitive Sciences, 2007, 11(12): 520–527
[181]
Divvala S K, Hoiem D, Hays J H, Efros A A, Hebert M.An empirical study of context in object detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009, 1271–1278
[182]
Yang M, Wu Y, Hua G.Context-aware visual tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(7):1195–1209
[183]
Li Y, Nevatia R.Key object driven multi-category object recognition,localization and tracking using spatio-temporal context. In: Proceedings of Europian Conference on Computer Vision. 2008, 409–422
[184]
Nguyen H T, Ji Q, Smeulders A W M.Spatio-temporal context for robust multitarget tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(1): 52–64
[185]
Wen L, Cai Z, Lei Z, Yi D, Li S.Robust online learned spatio-temporal context model for visual tracking. IEEE Transactions on Image Processing,2014, 23(2): 785–796
[186]
Grabner H, Matas J, Van Gool L, Cattin P.Tracking the invisible:Learning where the object might be. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 1285–1292
[187]
Wu Z, Hristov N I, Hedrick T L, Kunz T H, Betke M.Tracking a large number of objects from multiple views. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 1546–1553
[188]
Sugimura D, Kitani KM, Okabe T, Sato Y, Sugimoto A.Using individuality to track individuals: clustering individual trajectories in crowds using local appearance and frequency trait. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 1467–1474
[189]
Ali S, Shah M.Floor fields for tracking in high density crowd scenes.Lecture Notes in Computer Science. 2008, 5303: 1–14
[190]
Zhao T, Nevatia R.Tracking multiple humans in crowded environment.In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2004, 406–413
[191]
Betke M, Hirsh D E, Bagchi A, Hristov N I, Makris N C, Kunz TH.Tracking large variable numbers of objects in clutter. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2007, 1–8
[192]
Li Y, Huang C, Nevatia R.Learning to associate: Hybridboosted multitarget tracker for crowded scene. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2009, 2953–2960
[193]
Wu B, Nevatia R.Tracking of multiple, partially occluded humans based on static body part detection. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2006, 951–958
[194]
Brostow G J, Cipolla R.Unsupervised Bayesian detection of independent motion in crowds. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2006, 594–601
[195]
Pellegrini S, Ess A, Schindler K, Van Gool L.You’ll never walk alone:Modeling social behavior for multi-target tracking. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 261–268
[196]
Rodriguez M, Ali S, Kanade T.Tracking in unstructured crowded scenes. In: Proceedings of IEEE International Conference on Computer Vision. 2009, 1389–1396
[197]
Kratz L, Nishino K.Tracking with local spatio-temporal motion patterns in extremely crowded scenes. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010, 693–700
[198]
Rodriguez M, Sivic J, Laptev I, Audibert J Y.Data-driven crowd analysis in videos. In: Proceedings of IEEE International Conference on Computer Vision. 2011, 1235–1242
[199]
Idrees H, Warner N, Shah M.Tracking in dense crowds using prominence and neighborhood motion concurrence. Image and Vision Computing,2014, 32(1): 14–26
[200]
Zhang L, Maaten v. d L.Structure preserving object tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2013, 1838–1845
[201]
Zhu F, Wang X, Yu N.Crowd tracking with dynamic evolution of group structures. In: Proceedings of the 13th European Conference on Computer Vision–ECCV. 2014, 139–154
[202]
Gao Y, Ji R, Zhang L, Hauptmann A.Symbiotic tracker ensemble towards a unified tracking framework. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(7): 1122–1131
[203]
Zhong B, Yao H, Chen S, Ji R, Chin T J, Wang H.Visual tracking via weakly supervised learning from multiple imperfect oracles. Pattern Recognition, 2014, 47(3): 1395–1410
[204]
Yao A, Lin X, Wang G, Yu S.A compact association of particle filtering and kernel based object tracking. Pattern Recognition, 2012, 45(7):2584–2597
[205]
Henriques J F, Caseiro R, Martins P, Batista J.Exploiting the circulant structure of tracking-by-detection with kernels. In: Proceedings of the 12th European Conference on Computer Vision—ECCV 2012. 2012,702–715
[206]
Wu Y, Lim J, Yang MH.Online object tracking: a benchmark. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.2013, 2411–2418
[207]
Ross D A, Lim J, Lin R S, Yang M H.Incremental learning for robust visual tracking. International Journal of Computer Vision, 2008,77(1-3): 125–141
[208]
Kwon J, Lee K M.Visual tracking decomposition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2010,1269–1276
[209]
Wang Y, Qi Y, Li Y.Memory-based multiagent coevolution modeling for robust moving object tracking. The Scientific World Journal, 2013,2013
[210]
Wang Y, Qi Y.Memory-based cognitive modeling for robust object extraction and tracking. Applied Intelligence, 2013, 39(3): 614–629
[211]
Smith K, Ba S O, Odobez J M, Gatica-Perez D.Tracking the visual focus of attention for a varying number of wandering people. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008,30(7): 1212–1229

RIGHTS & PERMISSIONS

2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
AI Summary AI Mindmap
PDF(768 KB)

Accesses

Citations

Detail

Sections
Recommended

/