Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 23, Issue 5, Pages 2356-2368Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2014.2313227
Keywords
Object tracking; collaborative model; sparse representation; feature selection; occlusion handling
Funding
- Natural Science Foundation of China [61071209, 61272372]
- Joint Foundation of China Education Ministry [MCM20122071]
- Joint Foundation of China Mobile Communication Corporation [MCM20122071]
- National Science Foundation (NSF) CAREER [1149783]
- NSF Information and Intelligent Systems [1152576]
- Direct For Computer & Info Scie & Enginr [1152576, 1149783] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems [1149783, 1152576] Funding Source: National Science Foundation
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In this paper, we propose a robust object tracking algorithm based on a sparse collaborative model that exploits both holistic templates and local representations to account for drastic appearance changes. Within the proposed collaborative appearance model, we develop a sparse discriminative classifier (SDC) and sparse generative model (SGM) for object tracking. In the SDC module, we present a classifier that separates the foreground object from the background based on holistic templates. In the SGM module, we propose a histogram-based method that takes the spatial information of each local patch into consideration. The update scheme considers both the most recent observations and original templates, thereby enabling the proposed algorithm to deal with appearance changes effectively and alleviate the tracking drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.
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