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

• URL: http://arxiv.org/abs/2311.18839v1
• Date: Thu, 30 Nov 2023 18:59:56 GMT
• Title: TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios
• Authors: Lihao Liu, Yanqi Cheng, Zhongying Deng, Shujun Wang, Dongdong Chen, Xiaowei Hu, Pietro Li\`o, Carola-Bibiane Sch\"onlieb, Angelica Aviles-Rivero
• Abstract summary: Multi-object tracking in traffic videos offers immense potential for enhancing traffic monitoring accuracy and promoting road safety measures. Existing datasets for multi-object tracking in traffic videos often feature limited instances or focus on single classes. We introduce TrafficMOT, an extensive dataset designed to encompass diverse traffic situations with complex scenarios.
• Score: 23.831048188389026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 in driving advancements in the field of traffic monitoring and multi-object tracking.

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