An adaptive background model based on maximum statistical probability and a shadow suppression scheme for indoor and outdoor people detection by exploiting hue saturation value (HSV) color information is proposed. To obtain the initial background scene, the frequency of R, G, and B component values for each pixel at the same position in the learning sequence are respectively calculated; the R, G, and B component values with the biggest ratios are incorporated to model the initial background. The background maintenance, or the so-called background re-initiation, is also proposed to adapt to scene changes such as illumination changes and scene geometry changes. Moving cast shadows generally exhibit a challenge for accurate moving target detection. Based on the observation that a shadow cast on a background region lowers its brightness but does not change its chromaticity significantly, we address this problem in the article by exploiting HSV color information. In addition, quantitative metrics is introduced to evaluate the algorithm on a benchmark suite of indoor and outdoor video sequences. The experimental results are given to show the performance of the algorithm.
CHEN Baisheng
. Indoor and outdoor people detection and shadow
suppression by exploiting HSV color information[J]. Frontiers of Electrical and Electronic Engineering, 2008
, 3(4)
: 406
-410
.
DOI: 10.1007/s11460-008-0083-6
1. Kalman K P, von Brandt A . Moving object recognitionusing an adaptive background memory. In: Proceedings of Time-varying Image Processing and Moving Object RecognitionII. Netherlands: Amsterdam, 1990, 289–296
2. Stauffer C, Grimson W E L . Adaptive background mixturemodels for real-time tracking. In: Proceedingsof IEEE Conference on Computer Vision and Pattern Recognition, Colorado: Fort Collins. 1999, 2: 246–252
3. Prati A, Mikic I, Trivedi M M, et al.. Detecting moving shadows: algorithms and evaluation. IEEE Transactions on Pattern Analysis and MachineIntelligence, 2003, 25(7): 918–923. doi:10.1109/TPAMI.2003.1206520
4. Chung Y C, Wang J M, Chen S W . A vision-based traffic light detection system at intersections. Journal of Taiwan Normal University: Mathematics,Science and Technology, 2002, 47(1): 67–86
5. Massey M, Bender W . Salient stills: Process andPractice. IBM Systems Journal, 1996, 35(3&4): 557–573
6. Toyama K, Krumn J, Brumit B, et al.. Wallflower: principles and practice of backgroundMaintenance. In: Proceedings of the 7thIEEE International Conference on Computer Vision, 1999, 1: 255–261. doi:10.1109/ICCV.1999.791228
7. Herodotou N, Plataniotis K N, Venetsanopoulos A N . A color segmentation scheme for object-basedvideo coding. In: Proceedings of IEEE Symposiumon Advances in Digital Filtering and Signal Processing, 1998, 25–29