Moving target detection based on improved ghost suppression and adaptive visual background extraction

Abstract

Visual background extraction algorithm (ViBe) uses the first frame image to initialize the background model, which can easily introduce the “ghost”. Because ViBe uses the fixed segmentation threshold to achieve the foreground and background segmentation, the detection results in many false detections for the highly dynamic background. To solve these problems, an improved ghost suppression and adaptive Visual Background Extraction algorithm is proposed in this paper. Firstly, with the pixel’s temporal and spatial information, the historical pixels of a certain combination are used to initialize the background model in the odd frames of the video sequence. Secondly, the background sample set combined with the neighborhood pixels are used to determine a complex degree of the background, to acquire the adaptive segmentation threshold. Thirdly, the update rate is adjusted based on the complexity of the background. Finally, the detected result goes through a post-processing to achieve better detection results. The experimental results show that the improved algorithm will not only quickly suppress the “ghost”, but also have a better detection in a complex dynamic background.

Keywords

moving target detection / ghost suppression / adaptive visual background extraction

References

[1] SengarS S, MukhopadhyayS. Moving object detection based on frame difference and W4 [J]. Signal, Image and Video Processing, 2017, 11(7): 1357-1364

[2] YuX-h, ChenX-l, HuangY, ZhangL, GuanJ, HeYou. Radar moving target detection in clutter background via adaptive dual-threshold sparse Fourier transform [J]. IEEE Access, 2019, 7: 58200-58211

[3] LiX-l, SunZ, ZhangT-x, YiW, CuiG-l, KongL-jiang. WRFRFT-based coherent detection and parameter estimation of radar moving target with unknown entry/departure time [J]. Signal Processing, 2020, 166: 107228

[4] ZhouY, MaoA-l, HuoS-w, LeiJ-j, KungS-yuan. Salient object detection via fuzzy theory and object-level enhancement [J]. IEEE Transactions on Multimedia, 2019, 21(1): 74-85

[5] BaeG, ChoS I, KangS J, KimY H. Dual-dissimilarity measure-based statistical video cut detection [J]. Journal of Real-Time Image Processing, 2019, 16(6): 1987-1997

[6] MahrazM A, RiffiJ, TairiH. High accuracy optical flow estimation based on PDE decomposition [J]. Signal, Image and Video Processing, 2015, 9(6): 1409-1418

[7] YangY-p, YangZ-z, LiJ-l, FanLu. Foreground-background separation via generalized nuclear norm and structured sparse norm based low-rank and sparse decomposition [J]. IEEE Access, 2020, 884217-84229

[8] ZhaoC-q, SainA, QuY, GeY-x, HuH-bo. Background subtraction based on integration of alternative cues in freely moving camera [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2019, 29(7): 1933-1945

[9] ChenY-q, SunZ-l, LamK M. An effective subsuperpixel-based approach for background subtraction [J]. IEEE Transactions on Industrial Electronics, 2020, 67(1): 601-609

[10] LeM Y, AngeliniE D, Olivo-MarinJ C. An unbiased risk estimator for image denoising in the presence of mixed Poisson-Gaussian noise [J]. IEEE Transactions on Image Processing, 2014, 23(6): 2750-2750

[11] ShahM, DengJ D, WoodfordB J. A self-adaptive codeBook (SACB) model for real-time background subtraction [J]. Image and Vision Computing, 2015, 38: 52-64

[12] ZHU Bing, TIAN Lian-fang, DU Qi-liang, YU Lu-bin, SHI Li-xin. An improved background modeling algorithm based on the codebook model [C]//Chinese Control and Decision Conference(CCDC). Chongqing, 2017: 3998–4003.

[13] GAO Jun, ZHU Hong-hui. Moving object detection for video surveillance based on improved ViBe [C]//Chinese Control and Decision Conference(CCDC). Yinchuan, 2016: 6259–6263.

[14] LiuZ-k, AnD-x, HuangX-tao. Moving target shadow detection and global background reconstruction for video SAR based on single-frame imagery [J]. IEEE Access, 2019, 7: 42418-42425

[15] CONTE D, FOGGIA P, PERCANNELLA G, TUFANO F, VENTO M. An experimental evaluation of foreground detection algorithms in real scenes [J]. EURASIP Journal on Advances in Signal Processing, 2010: 373941. DOI: https://doi.org/10.1155/2010/373941.

[16] LüJ-q, LiuL-c, HaoL-g, ZhangW-zhong. Adaptive moving object extraction algorithm based on visual background extractor [J]. Journal of Application, 2015, 35(7): 2029-2032(in Chinese)

[17] CHUN-HYOK P, ZHAO Hai, ZHU Hong-bo, PAN Yi-lin. A novel motion detection approach based on the improved ViBe algorithm [C]//Chinese Control and Decision Conference. Yinchuan, 2016: 7081–7086.

[18] CHANG Le, LIU Zheng-hua, REN Yan. Improved adaptive ViBe and the application for segmentation of complex background [J]. Mathematical Problems in Engineering, 2016: 3835952. DOI: https://doi.org/10.1155/2016/3835952.

[19] FanZ-h, WangHui. Robust moving object detection based on spatio-temporal confidence relationship [J]. Electronics Letters, 2016, 52(10): 825-827

[20] YinB-c, ZhangJ, WangZ-fu. Background segmentation of dynamic scenes based on dual model [J]. IET Computer Vision, 2014, 8(6): 545-555

[21] van DroogenbroeckM, PaquotO. Background subtraction: Experiments and improvements for ViBe [C]. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), 2012, RI, Providence, 3237

[22] QuZ, HuangX-ling. The foreground detection algorithm combined the temporal-spatial information and adaptive visual background extraction [J]. Imaging Science Journal, 2017, 65(1): 49-61

[23] QuZ, HuangX, LiuLing. An improved algorithm of multi-exposure image fusion by detail enhancement [J]. Multimedia Systems, 2020, 27(1): 33-44

[24] QuZ, LiJ, BaoK-h, SiZ-chao. An unordered image stitching method based on binary tree and estimated overlapping area [J]. IEEE Transactions on Image Processing, 2020, 29: 6734-6744

[25] QuZ, ChenS-q, LiuY-q, LiuLing. Linear seam elimination of tunnel crack images based on statistical specific pixels ratio and adaptive fragmented segmentation [J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(9): 3599-3607

[26] ZhangR-g, LiuX-j, HuJ, ChangK, LiuKun. A fast method for moving object detection in video surveillance image [J]. Signal, Image and Video Processing, 2017, 11(5): 841-848