  We propose a new video camouflaged object detection (VCOD) framework that can
exploit both short-term dynamics and long-term temporal consistency to detect
camouflaged objects from video frames. An essential property of camouflaged
objects is that they usually exhibit patterns similar to the background and
thus make them hard to identify from still images. Therefore, effectively
handling temporal dynamics in videos becomes the key for the VCOD task as the
camouflaged objects will be noticeable when they move. However, current VCOD
methods often leverage homography or optical flows to represent motions, where
the detection error may accumulate from both the motion estimation error and
the segmentation error. On the other hand, our method unifies motion estimation
and object segmentation within a single optimization framework. Specifically,
we build a dense correlation volume to implicitly capture motions between
neighbouring frames and utilize the final segmentation supervision to optimize
the implicit motion estimation and segmentation jointly. Furthermore, to
enforce temporal consistency within a video sequence, we jointly utilize a
spatio-temporal transformer to refine the short-term predictions. Extensive
experiments on VCOD benchmarks demonstrate the architectural effectiveness of
our approach. We also provide a large-scale VCOD dataset named MoCA-Mask with
pixel-level handcrafted ground-truth masks and construct a comprehensive VCOD
benchmark with previous methods to facilitate research in this direction.
Dataset Link: https://xueliancheng.github.io/SLT-Net-project.
