Open Access

ARTICLE

Efficient Spatiotemporal Information Utilization for Video Camouflaged Object Detection

Dongdong Zhang, Chunping Wang, Huiying Wang, Qiang Fu^*

Army Engineering University of PLA, Shijiazhuang, 050003, China

* Corresponding Author: Qiang Fu. Email:

Computers, Materials & Continua 2025, 82(3), 4319-4338. https://doi.org/10.32604/cmc.2025.060653

Received 06 November 2024; Accepted 20 December 2024; Issue published 06 March 2025

Abstract

Video camouflaged object detection (VCOD) has become a fundamental task in computer vision that has attracted significant attention in recent years. Unlike image camouflaged object detection (ICOD), VCOD not only requires spatial cues but also needs motion cues. Thus, effectively utilizing spatiotemporal information is crucial for generating accurate segmentation results. Current VCOD methods, which typically focus on exploring motion representation, often ineffectively integrate spatial and motion features, leading to poor performance in diverse scenarios. To address these issues, we design a novel spatiotemporal network with an encoder-decoder structure. During the encoding stage, an adjacent space-time memory module (ASTM) is employed to extract high-level temporal features (i.e., motion cues) from the current frame and its adjacent frames. In the decoding stage, a selective space-time aggregation module is introduced to efficiently integrate spatial and temporal features. Additionally, a multi-feature fusion module is developed to progressively refine the rough prediction by utilizing the information provided by multiple types of features. Furthermore, we incorporate multi-task learning into the proposed network to obtain more accurate predictions. Experimental results show that the proposed method outperforms existing cutting-edge baselines on VCOD benchmarks.

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Keywords

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