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.

该研究实施了一个三阶段的视频分析框架，用于从道路上的摄像头提取高分辨率的交通数据，关键组成部分包括目标识别、透视转换和车辆轨迹重建，可以用于改善交通管理、识别危险驾驶行为以及减少事故和伤亡风险。

利用闭路电视摄像头收集高分辨率交通数据的二维单应性应用

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

Despite the widespread deployment of outdoor cameras, their potential for
automated analysis remains largely untapped due, in part, to calibration
challenges. The absence of precise camera calibration data, including intrinsic
and extrinsic parameters, hinders accurate real-world distance measurements
from captured videos. To address this, we present a scalable framework that
utilizes street-level imagery to reconstruct a metric 3D model, facilitating
precise calibration of in-the-wild traffic cameras. Notably, our framework
achieves 3D scene reconstruction and accurate localization of over 100 global
traffic cameras and is scalable to any camera with sufficient street-level
imagery. For evaluation, we introduce a dataset of 20 fully calibrated traffic
cameras, demonstrating our method's significant enhancements over existing
automatic calibration techniques. Furthermore, we highlight our approach's
utility in traffic analysis by extracting insights via 3D vehicle
reconstruction and speed measurement, thereby opening up the potential of using
outdoor cameras for automated analysis.

通过利用街景图像重建度量的三维模型并精确校准 100 多个全球交通摄像头，本研究展示了一个可扩展的框架，以解决基于野外摄像头的精确校准挑战，并通过提取信息进行交通分析，从而开发出利用室外摄像头进行自动分析的潜力。

实现全球范围的交通摄像头校准

Toward Planet-Wide Traffic Camera Calibration

Any intelligent traffic monitoring system must be able to detect anomalies
such as traffic accidents in real time. In this paper, we propose a
Decision-Tree - enabled approach powered by Deep Learning for extracting
anomalies from traffic cameras while accurately estimating the start and end
time of the anomalous event. Our approach included creating a detection model,
followed by anomaly detection and analysis. YOLOv5 served as the foundation for
our detection model. The anomaly detection and analysis step entail traffic
scene background estimation, road mask extraction, and adaptive thresholding.
Candidate anomalies were passed through a decision tree to detect and analyze
final anomalies. The proposed approach yielded an F1 score of 0.8571, and an S4
score of 0.5686, per the experimental validation.

本文提出一种决策树驱动的深度学习方法，通过智能交通监测系统从交通摄像头中快速精确地提取异常事件并估计其起止时间，并通过实验验证取得了 F1 score：0.8571 和 S4 score：0.5686。