RAOD: A Benchmark for Road Abandoned Object Detection From Video Surveillance

Road abandoned objects are potential safety hazards in modern traffic transport, especially in highway scenes. Promptly detecting such obstacles on the road is of great significance for driving safety and Intelligent Transportation Systems (ITS). Current research primarily focuses on developing diverse road anomaly detection approaches to discriminate the unknown objects regarded as abandoned ones. However, previous efforts have been largely inadequate due to the absence of abundant datasets. In addition, prevailing benchmarks mainly provide data pertinent to autonomous driving, which might not effectively generalize to highway scenarios owing to camera perspective and scope limitations. To address these challenges, we introduce a large-scale Road Abandoned Object Detection (RAOD) benchmark derived from video surveillance. First, we collect abundant real-world video clips containing various potential abandoned object categories on the road from our commercial ITS, then assemble a road abandoned object dataset comprising 557 video sequences and 18,953 images with pixel-level manual annotations. Second, we conduct exhaustive evaluation experiments employing a range of baseline models from mainstream algorithms on our dataset to illustrate the performance of different approaches. Third, we propose a novel image segmentation framework based on an area-aware attention mechanism. Experimental results reveal that our method outperforms the UNet-based model by nearly 9% in terms of dice score. Our dataset represents the most extensive open-source resource dedicated to road abandoned object detection, accessible publicly at
https://github.com/yajunbaby/A-Benchmark-for-Road-Abandoned-Object-Detection-from-Video-Surveillance
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