# Green Video Camouflaged Object Detection

Source: [Green Video Camouflaged Object Detection](<https://www.sota2.com/research/paper/68fa76b01e209131cea6e75a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:45:57.319Z

Authors: Xinyu Wang, Hong-Shuo Chen, Zhiruo Zhou, Suya You, Azad M. Madni, C.-C. Jay Kuo

Published: 2025-01-19T01:42:00.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2501.10914>)

[PDF](<https://arxiv.org/pdf/2501.10914.pdf>)