Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras

• URL: http://arxiv.org/abs/2401.07220v1
• Date: Sun, 14 Jan 2024 07:33:14 GMT
• Title: Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras
• Authors: Linlin Zhang, Xiang Yu, Abdulateef Daud, Abdul Rashid Mussah, Yaw Adu-Gyamfi
• Abstract summary: 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.
• Score: 9.946460710450319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traffic cameras remain the primary source data for surveillance activities such as congestion and incident monitoring. To date, State agencies continue to rely on manual effort to extract data from networked cameras due to limitations of the current automatic vision systems including requirements for complex camera calibration and inability to generate high resolution data. This study implements a three-stage video analytics framework for extracting high-resolution traffic data such vehicle counts, speed, and acceleration from infrastructure-mounted CCTV cameras. The key components of the framework include object recognition, perspective transformation, and vehicle trajectory reconstruction for traffic data collection. First, a state-of-the-art vehicle recognition model is implemented to detect and classify vehicles. Next, to correct for camera distortion and reduce partial occlusion, an algorithm inspired by two-point linear perspective is utilized to extracts the region of interest (ROI) automatically, while a 2D homography technique transforms the CCTV view to bird's-eye view (BEV). Cameras are calibrated with a two-layer matrix system to enable the extraction of speed and acceleration by converting image coordinates to real-world measurements. Individual vehicle trajectories are constructed and compared in BEV using two time-space-feature-based object trackers, namely Motpy and BYTETrack. The results of the current study showed about +/- 4.5% error rate for directional traffic counts, less than 10% MSE for speed bias between camera estimates in comparison to estimates from probe data sources. Extracting high-resolution data from traffic cameras has several implications, ranging from improvements in traffic management and identify dangerous driving behavior, high-risk areas for accidents, and other safety concerns, enabling proactive measures to reduce accidents and fatalities.

Related papers

• Joint object detection and re-identification for 3D obstacle multi-camera systems [47.87501281561605]
  This research paper introduces a novel modification to an object detection network that uses camera and lidar information. It incorporates an additional branch designed for the task of re-identifying objects across adjacent cameras within the same vehicle. The results underscore the superiority of this method over traditional Non-Maximum Suppression (NMS) techniques.
  arXiv  Detail & Related papers  (2023-10-09T15:16:35Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video Surveillance [2.1076255329439304]
  Existing datasets in traffic accidents are either small-scale, not from surveillance cameras, not open-sourced, or not built for freeway scenes. After integration and annotation by various dimensions, a large-scale traffic accidents dataset named TAD is proposed in this work.
  arXiv  Detail & Related papers  (2022-09-26T03:00:50Z)
• 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)
• 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)
• An overcome of far-distance limitation on tunnel CCTV-based accident detection in AI deep-learning frameworks [0.0]
  Tunnel CCTVs are installed to low height and long-distance interval. It is almost impossible to detect vehicles in far distance from the CCTV in the existing tunnel CCTV-based accident detection system. This paper creates each dataset consisting of images and bounding boxes based on the original and warped images of the CCTV.
  arXiv  Detail & Related papers  (2021-07-22T10:42:25Z)
• Fine-Grained Vehicle Perception via 3D Part-Guided Visual Data Augmentation [77.60050239225086]
  We propose an effective training data generation process by fitting a 3D car model with dynamic parts to vehicles in real images. Our approach is fully automatic without any human interaction. We present a multi-task network for VUS parsing and a multi-stream network for VHI parsing.
  arXiv  Detail & Related papers  (2020-12-15T03:03:38Z)
• Vehicle-Human Interactive Behaviors in Emergency: Data Extraction from Traffic Accident Videos [0.0]
  Currently, studying the vehicle-human interactive behavior in the emergency needs a large amount of datasets in the actual emergent situations that are almost unavailable. This paper provides a new yet convenient way to extract the interactive behavior data (i.e., the trajectories of vehicles and humans) from actual accident videos. The main challenge for data extraction from real-time accident video lies in the fact that the recording cameras are un-calibrated and the angles of surveillance are unknown.
  arXiv  Detail & Related papers  (2020-03-02T22:17:46Z)
• 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)
• Training-free Monocular 3D Event Detection System for Traffic Surveillance [93.65240041833319]
  Existing event detection systems are mostly learning-based and have achieved convincing performance when a large amount of training data is available. In real-world scenarios, collecting sufficient labeled training data is expensive and sometimes impossible. We propose a training-free monocular 3D event detection system for traffic surveillance.
  arXiv  Detail & Related papers  (2020-02-01T04:42:57Z)

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.