Video Camouflaged Object Detection (VCOD) aims to segment objects visually indistinguishable from their background in unconstrained video sequences. This task is challenging due to the extremely low appearance contrast and motion ambiguity caused by camera shifts, occlusion, and background clutter. Although motion cues provide crucial signals to break camouflage, existing methods typically adopt either implicit motion modeling based on initial semantic representations or explicit modeling based on low-level pixel motion, both of which suffer from information loss or noise sensitivity. To this end, we propose IDM-VCOD, an integrated dual-motion VCOD framework that unifies two complementary motion modeling strategies through a dual-motion integrated design. The implicit path aggregates temporal context via spatio-temporal prediction cubes for temporal neighborhood refinement, while the explicit path explicitly aligns multi-frame backgrounds to highlight foreground motion. A selective activation mechanism adaptively triggers the explicit branch only when implicit predictions are unreliable, enhancing accuracy and efficiency. Extensive experiments on the MoCA-Mask and CAD datasets demonstrate that IDM-VCOD achieves superior detection accuracy and generalization compared to state-of-the-art VCOD methods, while significantly reducing model size and inference cost. Comprehensive ablation studies further validate the effectiveness of the dual-motion design and its robustness in challenging camouflage scenarios.
Wang et al. (Wed,) studied this question.