Multi-cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation
Multi-cue Visual Tracking Using Robust Feature-Level Fusion Based on Joint Sparse Representation
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DOI：
10.1109/cvpr.2014.156
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
X. Lan;A. J. Ma;P. Yuen
中科院分区：
文献类型：
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作者：
X. Lan;A. J. Ma;P. Yuen
多个特征的使用被证明是一种有效的跟踪方法，因为每个特征的限制可以被补偿。由于视频序列，特别是长序列视频可能会发生光照、遮挡、姿态等不同类型的变化，如何动态地选择合适的特征是该方法的关键问题之一。针对多线索视觉跟踪中的这一问题，提出了一种新的联合稀疏表示模型，用于鲁棒的特征级融合。该方法利用稀疏表示的优点，动态去除不可靠的特征进行融合跟踪。结果，获得了鲁棒的跟踪性能。公开视频上的实验结果表明，该方法优于现有的稀疏表示和基于融合的跟踪器。
The use of multiple features for tracking has been proved as an effective approach because limitation of each feature could be compensated. Since different types of variations such as illumination, occlusion and pose may happen in a video sequence, especially long sequence videos, how to dynamically select the appropriate features is one of the key problems in this approach. To address this issue in multi-cue visual tracking, this paper proposes a new joint sparse representation model for robust feature-level fusion. The proposed method dynamically removes unreliable features to be fused for tracking by using the advantages of sparse representation. As a result, robust tracking performance is obtained. Experimental results on publicly available videos show that the proposed method outperforms both existing sparse representation based and fusion-based trackers.