Vehicle counting without background modeling
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Multi-part sparse representation in random crowded scenes tracking
Pattern Recognition Letters
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Conventional video surveillance systems often have several shortcomings. First, target detection can't be accurate under the light variation environment or clustering backgrounds. Second, multiple targets tracking become difficult on a crowd scene because the split and merge or occlusions among the tracked targets occur frequently and irregularly. Third, it is difficult to the partition the tracked targets from a merged image blob and then the target tracking may fail. Finally, the tracking efficiency and precision are reduced by the inaccurate foreground detection. In this study, the spatial-temporal probability background model, multi-mode tracking scheme, color-based difference projection, and ground point detection are proposed to improve the abovementioned problems. Experimental results show that the targets in the crowd scene may be tracked with the correct tracking modes and with rate above 15fps.