Likelihood-based object detection and object tracking using color histograms and EM

The topic of this paper is the integration of expectation maximization (EM) background modeling and template matching using color histograms as templates to improve person tracking for surveillance applications. The tracked objects are humans, which are not rigid bodies. As such shape deformations of the objects must be allowed. For each frame, the decision has to be made which pixels belong to an object, and which do not. The integration of detection and tracking is done using a likelihood-based framework. This way the classification of pixels between background and object can be based on comparing likelihoods rather then separate thresholds. A demonstration of the proposed algorithm is given.