EBPersons: A Dataset for Person Detection at the Edges of Buildings

With the increasing prevalence of buildings, incidents of falling from heights have become more and more frequent. Accurately detecting individuals at the edges of buildings through surveillance videos is crucial for timely intervention and accident prevention. However, this task, termed Person Detection at the Edges of Buildings (PDEB), presents significant challenges including variations in lighting conditions, occlusions, and small size of person instances. Existing person detection datasets are inadequate for PDEB due to domain gaps. To address this issue, we construct EBPersons, a completely new dataset specifically designed for PDEB. Comprising 1,314 videos captured across over 300 diverse building scenes with diverse lighting conditions, EBPersons provides a rich and challenging benchmark for PDEB research. Furthermore, we propose a baseline method specifically designed for PDEB, named STASH, which includes three key components: a Scale Match strategy to improve small object detection, a Temporal ROI Align Operator to leverage temporal context, and a Sequential-level Semantics Aggregation head to enhance feature representation. Extensive experiments are conducted on EBPersons to compare our method with other detectors, including generic object detectors, pedestrian detectors, and video object detectors. The results demonstrate the superior performance of the proposed STASH, providing a strong baseline for future research on PDEB.