Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack

Viktor Kocur, Milan Ftacnik; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 4009-4015

Abstract

In this paper we present our approach to the Track 1 of the 2021 AI City Challenge. The goal of the challenge track is to to analyse footage captured with traffic cameras by counting the number of vehicles performing various pre-defined motions of interest. Our approach is based on the CenterTrack object detection and tracking neural network used in conjunction with a simple IoU-based tracking algorithm. In the public evaluation server our system achieved the S1 score of 0.8449 placing it at the 8th place on the public leaderboard.

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[bibtex]
@InProceedings{Kocur_2021_CVPR, author = {Kocur, Viktor and Ftacnik, Milan}, title = {Multi-Class Multi-Movement Vehicle Counting Based on CenterTrack}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, month = {June}, year = {2021}, pages = {4009-4015} }