Adaptive Mean-Shift Tracking With Auxiliary Particles
Adaptive Mean-Shift Tracking With Auxiliary Particles
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DOI：
10.1109/tsmcb.2009.2021482
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发表时间：
2009-12-01
影响因子：
--
通讯作者：
Yagi, Yasushi
中科院分区：
文献类型：
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作者：
Wang, Junqiu;Yagi, Yasushi
将Mean-Shift算法的效率与粒子滤波的多假设特性自适应地结合起来，提出了一种新的稳健有效的跟踪方法。提出的算法的目的是处理突然运动和分心带来的问题。Mean-Shift跟踪算法是稳健和有效的，当目标的表示具有足够的区分性时，目标不会跳过带宽，并且不存在严重的干扰。提出了一种新的高效可靠的两阶段运动估计方法。如果运动估计器检测到突然运动，可以使用一些基于粒子滤波的跟踪器来超越Mean-Shift算法，但代价是使用较大的粒子集。在我们的方法中，只要提供合理的性能，就使用Mean-Shift算法。当检测到这种威胁时，引入辅助粒子来应对分心和突然移动。此外，在不存在威胁的情况下，根据前景和背景分布的分离来选择区分特征。这一策略很重要，因为当跟踪处于非稳定状态时，更新目标模型是危险的。我们通过与其他跟踪器在跟踪几个具有挑战性的图像序列上的比较，展示了该方法的性能。
We present a new approach for robust and efficient tracking by incorporating the efficiency of the mean-shift algorithm with the multihypothesis characteristics of particle filtering in an adaptive manner. The aim of the proposed algorithm is to cope with problems that were brought about by sudden motions and distractions. The mean-shift tracking algorithm is robust and effective when the representation of a target is sufficiently discriminative, the target does not jump beyond the bandwidth, and no serious distractions exist. We propose a novel two-stage motion estimation method that is efficient and reliable. If a sudden motion is detected by the motion estimator, some particle-filtering-based trackers can be used to outperform the mean-shift algorithm, at the expense of using a large particle set. In our approach, the mean-shift algorithm is used, as long as it provides reasonable performance. Auxiliary particles are introduced to cope with distractions and sudden motions when such threats are detected. Moreover, discriminative features are selected according to the separation of the foreground and background distributions when threats do not exist. This strategy is important, because it is dangerous to update the target model when the tracking is in an unsteady state. We demonstrate the performance of our approach by comparing it with other trackers in tracking several challenging image sequences.