Object tracking by detection for video surveillance systems based on modified codebook foreground detection and particle filter

In this paper, a novel approach is proposed to achieve the multi-object tracking in video surveillance system using a combination of tracking by detection method. For the foreground objects detection part, we implement a modified codebook model. First, the block-based model upgrades the pixel-based codebook model to block level, thus improving the processing speed and reducing memory. Moreover, by adding the orientation and magnitude of the block gradient, the codebook model contains not only information of color, but also the texture feature in order to further reduce noises and refine more entire foreground regions. For the tracking aspect, we further utilize the data from the foreground detection that a color-edgetexture histogram is used by calculate the local binary pattern of the edge of the foreground objects which could have a good performance in describing the shape and texture of the objects. Finally, occlusion solutions strategies are applies to order to overcome the occlusion problems during tracking. Experimental results on different data sets prove that our method has better performance and good real-time ability.