Multi-object tracking in traffic videos is a crucial research area, offering
immense potential for enhancing traffic monitoring accuracy and promoting road
safety measures through the utilisation of advanced machine learning
algorithms. However, existing datasets for multi-object tracking in traffic
videos often feature limited instances or focus on single classes, which cannot
well simulate the challenges encountered in complex traffic scenarios. To
address this gap, we introduce TrafficMOT, an extensive dataset designed to
encompass diverse traffic situations with complex scenarios. To validate the
complexity and challenges presented by TrafficMOT, we conducted comprehensive
empirical studies using three different settings: fully-supervised,
semi-supervised, and a recent powerful zero-shot foundation model Tracking
Anything Model (TAM). The experimental results highlight the inherent
complexity of this dataset, emphasising its value in driving advancements in
the field of traffic monitoring and multi-object tracking.

多目标跟踪在交通视频中是一个关键的研究领域，通过利用先进的机器学习算法，可以提高交通监控的准确性并促进道路安全措施。然而，现有的交通视频多目标跟踪数据集通常具有有限的实例或专注于单个类别，无法很好地模拟复杂交通场景中遇到的挑战。为了填补这一空白，我们介绍了 TrafficMOT，一个广泛的数据集，旨在涵盖具有复杂场景的多种交通情况。通过使用三种不同的设置来验证 TrafficMOT 所呈现的复杂性和挑战：完全监督、半监督和最近强大的零样本基础模型 Tracking Anything Model（TAM）。实验结果突出了该数据集的内在复杂性，强调其在交通监控和多目标跟踪领域推动发展的价值。

TrafficMOT：复杂交通场景下的多目标追踪挑战数据集

TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex  Traffic Scenarios

Due to its relevance in intelligent transportation systems, anomaly detection
in traffic videos has recently received much interest. It remains a difficult
problem due to a variety of factors influencing the video quality of a
real-time traffic feed, such as temperature, perspective, lighting conditions,
and so on. Even though state-of-the-art methods perform well on the available
benchmark datasets, they need a large amount of external training data as well
as substantial computational resources. In this paper, we propose an efficient
approach for a video anomaly detection system which is capable of running at
the edge devices, e.g., on a roadside camera. The proposed approach comprises a
pre-processing module that detects changes in the scene and removes the
corrupted frames, a two-stage background modelling module and a two-stage
object detector. Finally, a backtracking anomaly detection algorithm computes a
similarity statistic and decides on the onset time of the anomaly. We also
propose a sequential change detection algorithm that can quickly adapt to a new
scene and detect changes in the similarity statistic. Experimental results on
the Track 4 test set of the 2021 AI City Challenge show the efficacy of the
proposed framework as we achieve an F1-score of 0.9157 along with 8.4027 root
mean square error (RMSE) and are ranked fourth in the competition.

本文提出了一种高效的边缘设备上运行的视频异常检测系统，包括变化检测、背景建模和目标检测模块以及回溯异常检测算法，同时还提出了一种顺序变化检测算法。实验结果表明，该方法在 2021 年 AI City Challenge 中取得了 0.9157 的 F1 分数并排名第四。

交通视频异常检测的高效方法

An Efficient Approach for Anomaly Detection in Traffic Videos

Time-stamp aware anomaly detection in traffic videos is an essential task for
the advancement of the intelligent transportation system. Anomaly detection in
videos is a challenging problem due to sparse occurrence of anomalous events,
inconsistent behavior of a different type of anomalies and imbalanced available
data for normal and abnormal scenarios. In this paper, we present a three-stage
pipeline to learn the motion patterns in videos to detect a visual anomaly.
First, the background is estimated from recent history frames to identify the
motionless objects. This background image is used to localize the
normal/abnormal behavior within the frame. Further, we detect an object of
interest in the estimated background and categorize it into anomaly based on a
time-stamp aware anomaly detection algorithm. We also discuss the challenges
faced in improving performance over the unseen test data for traffic anomaly
detection. Experiments are conducted over Track 3 of NVIDIA AI city challenge
2019. The results show the effectiveness of the proposed method in detecting
time-stamp aware anomalies in traffic/road videos.

本文提出了一种三阶段管道模型，通过学习视频中的运动模式来检测视觉异常，并使用时间戳感知的异常检测算法将目标物体分类为异常，实验结果表明该方法能够有效检测交通路况视频中的时间戳感知异常。