基于侵入后时间分析的多摄像头图像分割提升道路安全
Enhancing Road Safety Through Multi-Camera Image Segmentation with Post-Encroachment Time Analysis
AI总结：
该研究提出基于YOLOv11分割的多摄像头视觉框架，通过像素级PET算法实现高分辨率实时交叉口安全评估，可识别高风险区域，为智能交通系统提供可扩展方案。
摘要：
信号交叉口的交通安全分析对减少车辆与行人碰撞至关重要，但传统基于事故的研究受限于数据稀疏性和报告延迟。本文提出一种用于实时安全评估的多摄像头计算机视觉框架，通过侵入后时间（Post-Encroachment Time, PET）计算实现，在美国加利福尼亚州丘拉维斯塔市H街与百老汇的交叉口进行了验证。四台同步摄像头提供连续视觉覆盖，帧在NVIDIA Jetson AGX Xavier边缘设备上处理，采用YOLOv11分割算法进行车辆检测。检测到的车辆多边形通过单应性变换转换为统一鸟瞰图，实现重叠摄像头视角的对齐。像素级PET算法通过测量连续车辆通过的时间，跟踪每个空间位置的时间占用情况，生成空间分辨率为3.3平方厘米的动态热图，实现细粒度危险可视化。带时间戳的车辆轨迹和PET数据存储在SQL数据库中用于纵向分析。多个时间间隔的结果表明，该框架能够识别高风险区域，具备亚秒级时间敏感性和实时边缘吞吐量，以平均2.68 FPS的速度生成800×800对数热图。本文验证了面向智能交通系统的去中心化视觉PET分析，并提出一种可扩展的高分辨率实时交叉口安全评估方法。
英文摘要：
Traffic safety analysis at signalized intersections is essential for reducing vehicle and pedestrian collisions, yet traditional crash-based studies are limited by data sparsity and reporting latency. This paper presents a multi-camera computer vision framework for real-time safety assessment through Post-Encroachment Time (PET) computation, demonstrated at the intersection of H Street and Broadway in Chula Vista, California. Four synchronized cameras provide continuous visual coverage, with frames processed on NVIDIA Jetson AGX Xavier edge devices using YOLOv11 segmentation for vehicle detection. Detected vehicle polygons are transformed into a unified bird's-eye map via homography, enabling alignment across overlapping camera views. A pixel-level PET algorithm tracks temporal occupancy at each spatial location by measuring the time between successive vehicle passages, enabling fine-grained hazard visualization through dynamic heatmaps with 3.3 sq-cm spatial resolution. Timestamped vehicle trajectories and PET data are stored in an SQL database for longitudinal analysis. Results across multiple time intervals demonstrate the framework's ability to identify high-risk regions with sub-second temporal sensitivity and real-time edge throughput, generating 800 x 800 logarithmic heatmaps at an average of 2.68 FPS. This paper validates decentralized vision-based PET analysis for intelligent transportation systems and presents a scalable methodology for high-resolution, real-time intersection safety evaluation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
