Depth information provides a strong cue for occlusion detection and handling, but has been largely omitted in generic object tracking until recently due to lack of suitable benchmark datasets and applications. In this work, we propose a Depth Masked Discriminative Correlation Filter (DM-DCF) which adopts novel depth segmentation based occlusion detection that stops correlation filter updating and depth masking which adaptively adjusts the spatial support for correlation filter. In Princeton RGBD Tracking Benchmark, our DM-DCF is among the state-of-the-art in overall ranking and the winner on multiple categories. Moreover, since it is based on DCF, ``DM-DCF`` runs an order of magnitude faster than its competitors making it suitable for time constrained applications.

翻译：深度信息为隔热检测和处理提供了强有力的提示,但直到最近,由于缺少合适的基准数据集和应用,通用对象跟踪中大部分被遗漏了。在这项工作中,我们建议采用深层遮蔽相异关系过滤器(DM-DCF),采用基于深度隔热的新式隔热检测,以阻止相关过滤器更新和深度遮蔽,从而适应性地调整对相关过滤器的空间支持。在普林斯顿 RGBD跟踪基准中,我们的DM-DCF在总体排名中名列第一,在多个类别中胜出。此外,由于它基于 DCF,“DM-DCF”比其竞争者能适应受时间限制的应用速度更快,“DM-DCF”的运行速度比竞争者快。