Object Detection and Tracking Using a Likelihood Based Approach
Paul J. Withagen, Klamer Schutte, Frans C. A. Groen, E.F. Deprettere, A.S.Z. Belloum, J.W.J. Heijnsdijk, F. van der Stappen · 2002
Many surveillance algorithms use both background modeling to detect moving objects and object tracking to analyze the motion patterns of the objects detected. In our case, Expectation Maximization (EM) is used to model the background and detect moving objects. Tracking is based on the objects color histogram. Using EM we can calculate the probability that a pixel value belongs to the background. Simultaneously, we use the color histogram of an object as a feature for tracking the object, which we use to calculate the probability that the pixel belongs to the object. In this paper we report integration between background modeling using EM and object tracking using color histograms. The classification between background objects will be based on probabilities. We will show the advantages for both the object detection and tracking part. 1