Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection

Xin Zhang, Tao Xiao, Ge-Peng Ji, Xuan Wu, Keren Fu, Qijun Zhao

Abstract: Camouflage poses notable challenges in distinguishing a static target, as it usually blends seamlessly with the background. However, any movement by the target can disrupt this disguise, making it detectable. Existing video camouflaged object detection (VCOD) approaches take noisy motion estimation as input or model motion implicitly, restricting detection performance in complex dynamic scenes. In this paper, we propose a novel Explicit Motion handling and Interactive Prompting framework for VCOD, dubbed EMIP, which handles motion cues explicitly using a frozen pre-trained optical flow fundamental model. EMIP is characterized by a two-stream architecture for simultaneously conducting camouflaged segmentation and optical flow estimation. Interactions across the dual streams are realized in an interactive prompting way that is inspired by emerging visual prompt learning. Two learnable modules, i.e. the camouflaged feeder and motion collector, are designed to incorporate segmentation-to-motion and motion-to-segmentation prompts, respectively, and enhance outputs of the both streams. The prompt fed to the motion stream is learned by supervising optical flow in a self-supervised manner. Furthermore, we show that long-term historical information can also be incorporated as a prompt into EMIP and achieve more robust results with temporal consistency. By leveraging promoting techniques based on EMIP, the proposed long-term model EMIP ${}^{\dagger }$ incurs lower training cost with only 8.5M trainable parameters (less than 8% of the total model parameters). Experimental results demonstrate that both EMIP and EMIP ${}^{\dagger }$ set new state-of-the-art records on popular VCOD benchmarks. Additionally, comparative evaluations against other video segmentation models on a wider range of video segmentation tasks demonstrate the robustness and superior generalization capabilities of EMIP. Our code is made publicly available at https://github.com/zhangxin06/EMIP

External IDs:dblp:journals/tip/ZhangXJWFZ25