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Type: Journal article
Title: Visual tracking with spatio-temporal Dempster-Shafer information fusion
Author: Li, X.
Dick, A.
Shen, C.
Zhang, Z.
Van Den Hengel, A.
Wang, H.
Citation: IEEE Transactions on Image Processing, 2013; 22(8):3028-3040
Publisher: IEEE-Inst Electrical Electronics Engineers Inc
Issue Date: 2013
ISSN: 1057-7149
Statement of
Xi Li, Anthony Dick, Chunhua Shen, Zhongfei Zhang, Anton van den Hengel, Hanzi Wang
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.
Keywords: Dempster- Shafer information fusion; Visual tracking; adaptive SVM learning; appearance model; multi-source discriminative learning
Rights: Copyright (c) 2013 IEEE.
RMID: 0020128837
DOI: 10.1109/TIP.2013.2253478
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Appears in Collections:Computer Science publications

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