@inproceedings{10.1145/3664647.3681153,
author = {Liu, Lihao and Cheng, Yanqi and Deng, Zhongying and Wang, Shujun and Chen, Dongdong and Hu, Xiaowei and Li\`{o}, Pietro and Sch\"{o}nlieb, Carola-Bibiane and Aviles-Rivero, Angelica},
title = {TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios},
year = {2024},
isbn = {9798400706868},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3664647.3681153},
doi = {10.1145/3664647.3681153},
abstract = {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 to drive advancements in the field of traffic monitoring and multi-object tracking. Code and data are available at the project page: https://lihaoliu-cambridge.github.io/trafficmot/},
booktitle = {Proceedings of the 32nd ACM International Conference on Multimedia},
pages = {1265–1273},
numpages = {9},
keywords = {foundation model, multi-object tracking, traffic video dataset},
location = {Melbourne VIC, Australia},
series = {MM '24}
}