Abstract: As traditional tracking algorithm based on compressive sensing is failed to track target stably when illumination and target position get seriously changed, a reformative target tracking algorithm based on locality sensitive histograms is proposed. By calculating the locality sensitive histograms, extracting illumination invariant features, the jointed features are used in compressive tracking. Results of tests on variant video sequences show that the proposed algorithm has advantages over compressive tracking and multiple instance learning tracking in target positions and lightings changing heavily. Obtained results satisfy the requirements of real-time tracking.

Key words: compressive tracking, drift, locality sensitive histograms, illumination invariant features, multiple instance learning

摘要： 压缩跟踪在光照发生剧烈变化和目标姿势变化较大时容易出现漂移甚至跟丢现象。针对此缺陷，提出基于局部敏感直方图的压缩跟踪。通过计算局部敏感直方图，提取光照不变特征，联合压缩跟踪中使用的特征得到更优的特征。对不同视频序列的跟踪结果表明，与压缩跟踪和多示例学习跟踪算法相比，提出的算法在目标姿势发生较大变化和光照变化剧烈的情况下能够实现稳定的跟踪，并且满足实时性要求。

关键词: 压缩跟踪, 漂移, 局部敏感直方图, 光照不变特征, 多示例学习