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Visual tracking via Boolean map representations
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DOI：10.1016/j.patcog.2018.03.029.png)
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摘要
En 中文
In this paper, we present a simple yet effective Boolean map based representation that exploits connectivity cues for visual tracking. We describe a target object with histogram of oriented gradients and raw color features, of which each one is characterized by a set of Boolean maps generated by uniformly thresholding their values. The Boolean maps effectively encode multi-scale connectivity cues of the target with different granularities. The fine-grained Boolean maps capture spatially structural details that are effective for precise target localization while the coarse-grained ones encode global shape information that are robust to large target appearance variations. Finally, all the Boolean maps form together a robust representation that can be approximated by an explicit feature map of the intersection kernel, which is fed into a logistic regression classifier with online update, and the target location is estimated within a particle filter framework. The proposed representation scheme is computationally efficient and facilitates achieving favorable performance in terms of accuracy and robustness against the state-of-the-art tracking methods on the OTB50 and VOT2016 benchmark datasets. (C) 2018 Elsevier Ltd. All rights reserved.
Keyword:
Visual tracking
Boolean map
Logistic regression
Intersection kernel
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期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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