  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.
