IDENTIFYING CONCEALED OBJECTS FROM VIDEOS

Abstract

Concealed objects are often hard to identify from still images, as often camouflaged objects exhibit patterns seamless to the background. In this work, we propose a novel video concealed object detection (VCOD) framework, called , as the concealed state is likely to break when the object moves. The proposed SLT-Net leverages on both short-term dynamics and long-term temporal consistency to detect concealed objects in continuous video frames. Unlike previous methods that often utilize homography or optical flows to explicitly represent motions, we build a dense correlation volume to implicitly capture motions between neighbouring frames. To enforce the temporal consistency within a video sequence, we utilize a spatial-temporal transformer to jointly refine the short-term predictions. Extensive experiments on existing image and VCOD benchmarks demonstrate the architectural effectiveness of our approach. We further collect a large-scale VCOD dataset named MoCA-Mask with pixel-level handcrafted ground-truth masks and construct a comprehensive VCOD benchmark with previous methods. Videos and codes can be found at: https://anonymous.4open.science/r/long-short-vcod-C0AF/README.md.

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Reproductions