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Object Tracking Algorithm based on Improved ContextModel in Combination with DetectionMechanismforSuspectedObjects
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DOI：10.1007/s11042-018-7025-y.png)
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摘要
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Built upon the methodology of spatio-temporal context, a simple yet robust object tracking method is proposed for solving the occlusion problems in this paper. This algorithm makes full use of the context information of the object and its local background to calculate the features, which maximumlly improve the occlusion predictive response and recapture accuracy. Firstly, an early warning mechanism is adopted to realize the occlusion detection. Once the object is fully occluded, the object position with accurate motion information saved in the early warning is predicted and memory tracking model is used to delete the suspected object region, which reduces the matching complexity. Finally, a confidence strategy for similarity measurement is adopted to capture the suspected object when the object appears, and the optimal confidence is introduced to get an adaptive update model. Many simulation experiments in benchmark videos show that our proposed algorithm achieves more favorable performance than these existing state-of-the-art algorithms.
Keyword:
Object tracking
Occlusion detection
Early warning
Context information
Confidence degree
Spatio-temporal feature
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