Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras
CoRR(2024)
摘要
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
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.
更多查看译文
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要