Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection
This work addresses tracking challenges in urban traffic for applications like surveillance, but it is incremental as it builds on existing detection methods.
The paper tackled multiple object tracking in urban traffic scenes by combining background subtraction and object detector detections, achieving competitive performance on the Urban tracker dataset.
In this paper, we propose to combine detections from background subtraction and from a multiclass object detector for multiple object tracking (MOT) in urban traffic scenes. These objects are associated across frames using spatial, colour and class label information, and trajectory prediction is evaluated to yield the final MOT outputs. The proposed method was tested on the Urban tracker dataset and shows competitive performances compared to state-of-the-art approaches. Results show that the integration of different detection inputs remains a challenging task that greatly affects the MOT performance.