"A PROBABILISTIC BAYESIAN FRAMEWORK FOR MODEL-BASED OBJECT TRACKING USING UNDECIMATED WAVELET PACKET DESCRIPTORS"

The paper presents a probabilistic Bayesian framework for object tracking using a combination of a corner-based model and coefficients of Undecimated wavelet packet transform (UWPT) inside a patch around each corner. This combination uses the UWPT coefficients patch helps to enrich the global representation of the object shape model by local descriptors. The goal is to maximize the posterior of the object global position. To this end, a voting mechanism is used based on the coherency among the model corners. The role of the local wavelet-based descriptors is to filter out some irrelevant observation before the voting process. Experimental results indicate good performances of the algorithm in crowd scenes and partial occlusions.