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.
CITATION STYLE
Ooi, H. L., Bilodeau, G. A., & Saunier, N. (2019). Tracking in urban traffic scenes from background subtraction and object detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11662 LNCS, pp. 195–206). Springer Verlag. https://doi.org/10.1007/978-3-030-27202-9_17
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