TUMTraf EMOT: Event-Based Multi-Object Tracking Dataset and Baseline for Traffic Scenarios
This work addresses a research gap in event-based vision for traffic scenarios, providing a dataset and benchmark for vehicle and pedestrian tracking, but it is incremental as it builds on existing tracking-by-detection methods.
The authors tackled the problem of poor multi-object tracking performance under dim lighting and high-speed conditions in Intelligent Transportation Systems by introducing an event-based dataset and baseline, achieving excellent performance as reported.
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.