Dense descriptor for visual tracking and robust update model strategy
Journal of Ambient Intelligence and Humanized Computing(2017)
摘要
Context analysis is a research field that is attracting growing interest in recent years, especially due to the encouraging results carried out by the semantic-based approach. Anyway, semantic strategies entail the use of trackers capable to show robustness to long-term occlusions, viewpoint changes and identity swap that represent the main problem of many tracking-by-detection solutions. This paper proposes a robust tracking-by-detection framework based on dense SIFT descriptors in combination with an ad-hoc target appearance model update able to overtake the discussed issues. The obtained performances show how our tracker competes with state-of-the-art results and manages occlusions, clutter, changes of scale, rotation and appearance, better than competing tracking methods.
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关键词
Visual tracking,Dense SIFT,RANSAC
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