  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.
