@inproceedings{
liu2024trafficmot,
title={Traffic{MOT}: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios},
author={Lihao Liu and Yanqi Cheng and Zhongying Deng and Shujun Wang and Dongdong Chen and Xiaowei Hu and Pietro Lio and Carola-Bibiane Sch{\"o}nlieb and Angelica I Aviles-Rivero},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=rY031lCoQd}
}
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.
TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios
Lihao Liu Yanqi Cheng Zhongying Deng Shujun Wang Dongdong Chen Xiaowei Hu Pietro Lio Carola-Bibiane Schönlieb Angelica I Aviles-Rivero