Visual tracking with spatio-temporal Dempster-Shafer information fusion

Abstract

A key problem in visual tracking is how to effectively combine spatio-temporal visual information from throughout a video to accurately estimate the state of an object. We address this problem by incorporating Dempster-Shafer information fusion into the tracking approach. To implement this fusion task, the entire image sequence is partitioned into spatially and temporally adjacent subsequences. A support vector machine (SVM) classifier is trained for object=non-object classification on each of these subsequences, the outputs of which act as separate data sources. To combine the discriminative information from these classifiers, we further present a spatio-temporal weighted Dempster-Shafer (STWDS) scheme. Moreover, temporally adjacent sources are likely to share discriminative information on object/non-object classification. In order to use such information, an adaptive SVM learning scheme is designed to transfer discriminative information across sources. Finally, the corresponding Dempster-Shafer belief function of the STWDS scheme is embedded into a Bayesian tracking model. Experimental results on challenging videos demonstrate the effectiveness and robustness of the proposed tracking approach.