Related papers: TUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios

TUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios

URL: http://arxiv.org/abs/2512.14595v2
Date: Sat, 20 Dec 2025 10:18:03 GMT
Title: TUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios
Authors: Mengyu Li, Xingcheng Zhou, Guang Chen, Alois Knoll, Hu Cao,
Abstract summary: We introduce an initial pilot dataset tailored for event-based ITS, covering vehicle and pedestrian detection and tracking.<n>We establish a tracking-by-detection benchmark with a specialized feature extractor based on this dataset, achieving excellent performance.
Score: 39.00650546356885
License: http://creativecommons.org/licenses/by/4.0/
Abstract: In Intelligent Transportation Systems (ITS), multi-object tracking is primarily based on frame-based cameras. However, these cameras tend to perform poorly under dim lighting and high-speed motion conditions. Event cameras, characterized by low latency, high dynamic range and high temporal resolution, have considerable potential to mitigate these issues. Compared to frame-based vision, there are far fewer studies on event-based vision. To address this research gap, we introduce an initial pilot dataset tailored for event-based ITS, covering vehicle and pedestrian detection and tracking. We establish a tracking-by-detection benchmark with a specialized feature extractor based on this dataset, achieving excellent performance.

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