Recognition of occluded pedestrians from the driver's perspective for extending sight distance and ensuring driving safety at signal-free intersections

Abstract

Urban intersections without traffic signals are prone to accidents involving motor vehicles and pedestrians. Utilizing computer vision technology to detect pedestrians crossing the street can effectively mitigate the occurrence of such accidents. Faced with the complex issue of pedestrian occlusion at signal-free intersections, this paper proposes a target detection model called Head feature And ENMS fusion Residual connection For CNN (HAERC). Specifically, the model includes a head feature module that detects occluded pedestrians by integrating their head features with the overall target. Additionally, to address the misselection caused by overlapping candidate boxes in two-stage target detection models, an Extended Non-Maximum Suppression classifier (ENMS) with expanded IoU thresholds is proposed. Finally, leveraging the CityPersons dataset and categorizing it into four classes based on occlusion levels (heavy, reasonable, partial, bare), the HAERC model is experimented on these classes and compared with baseline models. Experimental results demonstrate that HAERC achieves superior False Positives Per Image (FPPI) values of 46.64%, 9.59%, 9.43%, and 6.78% respectively for the four classes, outperforming all baseline models. The study concludes that the HAERC model effectively identifies occluded pedestrians in the complex environment of urban intersections without traffic signals, thereby enhancing safety for long-range driving at such intersections.

Graphical abstract

Keywords

Traffic safety / Signal-free intersections / Pedestrian crossing / Occlusion recognition / HAERC / ENMS