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AIC2018 Report: Traffic Surveillance Research
AbstractTraffic surveillance and management technologies are one of the most intriguing aspects of smart city applications. In this paper, we investigate and present the methods for vehicle detections, tracking, speed estimation and anomaly detection in NVIDIA AI City Challenge 2018 (AIC2018). We applied Mask-RCNN and deep-sort for vehicle detection and tracking in track 1, and optical flow based method in track 2. In track 1, we achieve 100% detection rate and 7.97 mile/hour estimation error for speed estimation.
Related Material
[pdf][bibtex]@InProceedings{Mao_2018_CVPR_Workshops,
author = {Mao, Tingyu and Zhang, Wei and He, Haoyu and Lin, Yanjun and Kale, Vinay and Stein, Alexander and Kostic, Zoran},
title = {AIC2018 Report: Traffic Surveillance Research},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}
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