COSOS-1k: A Benchmark Dataset and Occlusion-Aware Uncertainty Learning for Multi-View Video Object Detection

Wenjie Yang, Yueying Kao, Tong Liu, Yuanlong Yu, Kaiqi Huang

Abstract: Confined spaces refer to partially or fully enclosed areas, e.g., sewage wells, where working conditions pose significant risks to the workers. The evaluation of COfined Space Operational Safety (COSOS) refers to verifying whether workers are properly equipped with safety equipment before entering a confined space, which is crucial for protecting their safety and health. Due to the crowded nature of such environments and the small size of certain safety equipment, existing methods face significant challenges. Moreover, there is a lack of dedicated datasets to support research in this domain. In this paper, in order to advance research in this challenging task, we present COSOS-1k, an extensive dataset constructed from diverse confined space scenarios. It comprises multi-view videos for each scenario, covers 10 essential safety protective equipments and 6 attributes of worker, and is annotated with expressive object locations, fine-grained attributes, and occlusion status. The COSOS-1k is the first dataset known to date, tailored explicitly for the real-world COSOS scenarios. In addition, we address the challenge of occlusion from three perspectives: instance, video, and view. Firstly, at the instance level, we propose Occlusion-aware Uncertainty Estimation (OUE) method, which leverages box-level occlusion annotations to enable part-level occlusion prediction for objects. Secondly, at the video level, we introduce Cross-Frame Cluster (CFC) attention, which integrates temporal context features from the same object category to mitigate the impact of occlusions in the current frame. Finally, we extend CFC to the view level and form Cross-View Cluster (CVC) attention, where complementary information is mined from another view. Extensive experiments demonstrate the effectiveness of the proposed methods and provide insights into the importance of dataset diversity and expressivity. The COSOS-1k dataset and code are available at https://github.com/deepalchemist/cosos-1k

External IDs:dblp:journals/tip/YangKLYH26