Vehicle Detection and Tracking From Surveillance Cameras in Urban Scenes
This work addresses vehicle detection and tracking for traffic safety applications, but it is incremental as it builds on existing tracking-by-detection methods with added features.
The paper tackled challenges in multi-object tracking (MOT) for vehicles in urban scenes, such as long-term occlusions and fast motion, by extending an IOU-based tracker with re-identification features, resulting in outperforming the baseline on the UA-DETRAC benchmark while maintaining suitable processing speed for online use.
Detecting and tracking vehicles in urban scenes is a crucial step in many traffic-related applications as it helps to improve road user safety among other benefits. Various challenges remain unresolved in multi-object tracking (MOT) including target information description, long-term occlusions and fast motion. We propose a multi-vehicle detection and tracking system following the tracking-by-detection paradigm that tackles the previously mentioned challenges. Our MOT method extends an Intersection-over-Union (IOU)-based tracker with vehicle re-identification features. This allows us to utilize appearance information to better match objects after long occlusion phases and/or when object location is significantly shifted due to fast motion. We outperform our baseline MOT method on the UA-DETRAC benchmark while maintaining a total processing speed suitable for online use cases.