Enhancing Road Safety Through Multi-Camera Image Segmentation with Post-Encroachment Time Analysis

• URL: http://arxiv.org/abs/2511.12018v1
• Date: Sat, 15 Nov 2025 04:06:24 GMT
• Title: Enhancing Road Safety Through Multi-Camera Image Segmentation with Post-Encroachment Time Analysis
• Authors: Shounak Ray Chaudhuri, Arash Jahangiri, Christopher Paolini,
• Abstract summary: This paper presents a novel multi-camera computer vision framework for real-time safety assessment through Post-Encroachment Time (PET) computation.<n>Four synchronized cameras provide continuous visual coverage, with each frame processed on NVIDIA Jetson AGX Xavier devices.<n>PET algorithm measures vehicle position without reliance on fixed cells, allowing fine-grained hazard visualization via dynamic heatmaps.
• Score: 0.15293427903448018
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Traffic safety analysis at signalized intersections is vital for reducing vehicle and pedestrian collisions, yet traditional crash-based studies are limited by data sparsity and latency. This paper presents a novel 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 each frame processed on NVIDIA Jetson AGX Xavier devices using YOLOv11 segmentation for vehicle detection. Detected vehicle polygons are transformed into a unified bird's-eye map using homography matrices, enabling alignment across overlapping camera views. A novel pixel-level PET algorithm measures vehicle position without reliance on fixed cells, allowing fine-grained hazard visualization via dynamic heatmaps, accurate to 3.3 sq-cm. Timestamped vehicle and PET data is stored in an SQL database for long-term monitoring. Results over various time intervals demonstrate the framework's ability to identify high-risk regions with sub-second precision and real-time throughput on edge devices, producing data for an 800 x 800 pixel logarithmic heatmap at an average of 2.68 FPS. This study validates the feasibility of decentralized vision-based PET analysis for intelligent transportation systems, offering a replicable methodology for high-resolution, real-time, and scalable intersection safety evaluation.

Related papers

• Measuring Braking Behavior Using Vehicle Tracking and Camera-to-Satellite Homography Rectification [4.61803711540329]
  This paper presents an open-source software application for analyzing traffic camera footage, focusing on vehicle behavior and braking events at signalized urban highways.<n>The core innovation is a robust ground-plane homography estimation that links fixed traffic camera views to satellite orthoimagery.<n>All detection and trajectory data are stored in a ClickHouse database for subsequent analysis.
  arXiv  Detail & Related papers  (2026-01-24T18:59:32Z)
• An Optimized YOLOv5 Based Approach For Real-time Vehicle Detection At Road Intersections Using Fisheye Cameras [0.13092499936969584]
  Real time vehicle detection is a challenging task for urban traffic surveillance.<n>Fish eye cameras are widely used in real time vehicle detection purpose to provide large area coverage and 360 degree view at junctions.<n>To overcome challenges such as light glare from vehicles and street lights, shadow, non-linear distortion, scaling issues of vehicles and proper localization of small vehicles, a modified YOLOv5 object detection scheme is proposed.
  arXiv  Detail & Related papers  (2025-02-06T23:42:05Z)
• Homography Guided Temporal Fusion for Road Line and Marking Segmentation [73.47092021519245]
  Road lines and markings are frequently occluded in the presence of moving vehicles, shadow, and glare. We propose a Homography Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames for complementary cues. We show that exploiting available camera intrinsic data and ground plane assumption for cross-frame correspondence can lead to a light-weight network with significantly improved performances in speed and accuracy.
  arXiv  Detail & Related papers  (2024-04-11T10:26:40Z)
• Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras [9.946460710450319]
  This study implements a three-stage video analytics framework for extracting high-resolution traffic data from CCTV cameras. The key components of the framework include object recognition, perspective transformation, and vehicle trajectory reconstruction. The results of the study showed about +/- 4.5% error rate for directional traffic counts, less than 10% MSE for speed bias between camera estimates.
  arXiv  Detail & Related papers  (2024-01-14T07:33:14Z)
• Real-Time Accident Detection in Traffic Surveillance Using Deep Learning [0.8808993671472349]
  This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method. The robustness of the proposed framework is evaluated using video sequences collected from YouTube with diverse illumination conditions.
  arXiv  Detail & Related papers  (2022-08-12T19:07:20Z)
• Cross-Camera Trajectories Help Person Retrieval in a Camera Network [124.65912458467643]
  Existing methods often rely on purely visual matching or consider temporal constraints but ignore the spatial information of the camera network. We propose a pedestrian retrieval framework based on cross-camera generation, which integrates both temporal and spatial information. To verify the effectiveness of our method, we construct the first cross-camera pedestrian trajectory dataset.
  arXiv  Detail & Related papers  (2022-04-27T13:10:48Z)
• Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking [58.95210121654722]
  We propose a real-time city-scale multi-camera vehicle tracking system that handles real-world, low-resolution CCTV instead of idealized and curated video streams. Our method is ranked among the top five performers on the public leaderboard.
  arXiv  Detail & Related papers  (2022-04-15T12:47:01Z)
• Traffic-Net: 3D Traffic Monitoring Using a Single Camera [1.1602089225841632]
  We provide a practical platform for real-time traffic monitoring using a single CCTV traffic camera. We adapt a custom YOLOv5 deep neural network model for vehicle/pedestrian detection and an enhanced SORT tracking algorithm. We also develop a hierarchical traffic modelling solution based on short- and long-term temporal video data stream.
  arXiv  Detail & Related papers  (2021-09-19T16:59:01Z)
• Cross-Camera Feature Prediction for Intra-Camera Supervised Person Re-identification across Distant Scenes [70.30052164401178]
  Person re-identification (Re-ID) aims to match person images across non-overlapping camera views. ICS-DS Re-ID uses cross-camera unpaired data with intra-camera identity labels for training. Cross-camera feature prediction method to mine cross-camera self supervision information. Joint learning of global-level and local-level features forms a global-local cross-camera feature prediction scheme.
  arXiv  Detail & Related papers  (2021-07-29T11:27:50Z)
• Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model [43.850588717944916]
  Multi-target multi-camera tracking (MTMCT) is a crucial technique for smart city applications. We propose an effective and reliable MTMCT framework for vehicles. Our proposed MTMCT is evaluated on the CityFlow dataset and achieves a new state-of-the-art performance with IDF1 of 74.93%.
  arXiv  Detail & Related papers  (2020-08-22T08:54:47Z)
• Edge Computing for Real-Time Near-Crash Detection for Smart Transportation Applications [29.550609157368466]
  Traffic near-crash events serve as critical data sources for various smart transportation applications. This paper leverages the power of edge computing to address these challenges by processing the video streams from existing dashcams onboard in a real-time manner. It is among the first efforts in applying edge computing for real-time traffic video analytics and is expected to benefit multiple sub-fields in smart transportation research and applications.
  arXiv  Detail & Related papers  (2020-08-02T19:39:14Z)
• Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera [74.45649274085447]
  We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens. Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
  arXiv  Detail & Related papers  (2020-02-28T00:24:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.