Green Video Camouflaged Object Detection
This work addresses video-based camouflaged object detection, an incremental improvement focusing on reducing complexity and stabilizing performance for computer vision applications.
The paper tackles camouflaged object detection in videos by proposing GreenVCOD, which uses long- and short-term temporal neighborhoods to capture spatial/temporal context, achieving competitive performance compared to state-of-the-art benchmarks.
Camouflaged object detection (COD) aims to distinguish hidden objects embedded in an environment highly similar to the object. Conventional video-based COD (VCOD) methods explicitly extract motion cues or employ complex deep learning networks to handle the temporal information, which is limited by high complexity and unstable performance. In this work, we propose a green VCOD method named GreenVCOD. Built upon a green ICOD method, GreenVCOD uses long- and short-term temporal neighborhoods (TN) to capture joint spatial/temporal context information for decision refinement. Experimental results show that GreenVCOD offers competitive performance compared to state-of-the-art VCOD benchmarks.