PEDESTRIAN: An Egocentric Vision Dataset for Obstacle Detection on Pavements
Urban sidewalks are frequently obstructed by hazards that compromise pedestrian safety, yet real-time detection is hindered by the absence of high-quality, multi-class egocentric visual datasets. To address this gap, we introduce the first large-scale egocentric video dataset specifically designed for sidewalk obstacle detection—comprising 340 real-world smartphone-recorded videos spanning 29 common obstacle categories. We systematically define and publicly release a high-fidelity, fine-grained annotation benchmark, the first of its kind, thereby filling a critical void in open pedestrian safety resources. Leveraging this dataset, we conduct a comprehensive evaluation of state-of-the-art object detectors—including YOLOv8 and Mask R-CNN—establishing fully reproducible baselines. Our best-performing model achieves a mean average precision (mAP@0.5) of 68.3%. This work provides both an essential data foundation and an authoritative performance benchmark for developing robust pedestrian safety warning systems.