自适应区域协方差跟踪算法

An adaptive region based covariance tracking algorithm

协方差跟踪算法由于其优秀的特征描述能力在近年获得众多关注,但其全局遍历搜索策略使其仍不够高效。提出一个通用的、自适应的协方差跟踪算法,该算法利用了自适应积分区域计算策略和简单的遮挡检测处理方法,前者远快于积分图像计算并自适应于跟踪目标和跟踪环境,后者用于动态调整搜索窗口的大小。积分图像计算和全局协方差跟踪可以看作所提算法的一种特例。所提算法自然统一了局部搜索策略和全局搜索策略,并可根据跟踪环境(如遮挡、突然偏移)自然切换。所提算法既获得了在正常情况下局部搜索所带来的高效、偏离的健壮性和稳定的轨迹,又获得了在非正常情况下的由更大搜索窗口所带来的遮挡处理和重新识别定位目标的能力。通过在部分视频序列上的实验,所提算法展现出优秀的目标表达能力、更快的跟踪速度和更好的健壮性。

Covariance tracking has achieved impressive successes in recent years due to its competent region covariance-based feature descriptor. However, its brute-force search strategy is still inefficient. A generalized,adaptive covariance tracking algorithm is proposed, which uses novel integral region computation and occlusion detection. The integral region is much faster and adaptive to the tracking target and tracking condition. The adaptive search window is adjusted by simple occlusion detection. The integral image and the global covariance tracking can be seen as a special case of integral region and the proposed algorithm, respectively. The proposed algorithm unifies the local and global search strategies in an elegant way and smoothly switches them according to the tracking condition judged by occlusion detector. It gets much better efficiency and robustness for distraction and stable trajectory by local search in normal steady state, and obtains more abilities for occlusion and re-identification by enlarged search window （until to global search） in abnormal situation at the same time. Experiments on many video sequences show that the proposed algorithm has excellent target representation ability,faster speed,and more robustness.