A Video Analytic System for Rail Crossing Point Protection

With the rise of AI deep learning, video surveillance based on deep neural networks can provide real-time detection and tracking of vehicles and pedestrians. We present a video analytic system for monitoring railway crossing and providing security protection for rail intersections. Our system can automatically determine the rail-crossing gate status via visual detection and analyze traffic by detecting and tracking passing vehicles, thus to oversee a set of rail-transportation related safety events. Assuming a fixed camera view, each gate RoI can be manually annotated once for each site during system setup, and then gate status can be automatically detected afterwards. Vehicles are detected using YOLOv4 and multi-target tracking is performed using DeepSORT. Safety-related events including trespassing are continuously monitored using rule-based triggering. Experimental evaluation is performed on a Youtube rail crossing dataset as well as a private dataset. On the private dataset of 76 total minutes from 38 videos, our system can successfully detect all 56 events out of 58 annotated events. On the public dataset of 14.21 hrs of videos, it detects 58 out of 62 events.