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Computer Science > Computer Vision and Pattern Recognition

arXiv:2207.01183 (cs)
[Submitted on 4 Jul 2022 (v1), last revised 13 Jul 2022 (this version, v2)]

Title:Fast Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video using CNN and Bounding Box Propagation

Authors:Sandy Ardianto, Hsueh-Ming Hang, Wen-Huang Cheng (National Yang Ming Chiao Tung University)
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Abstract:We design a fast car detection and tracking algorithm for traffic monitoring fisheye video mounted on crossroads. We use ICIP 2020 VIP Cup dataset and adopt YOLOv5 as the object detection base model. The nighttime video of this dataset is very challenging, and the detection accuracy (AP50) of the base model is about 54%. We design a reliable car detection and tracking algorithm based on the concept of bounding box propagation among frames, which provides 17.9 percentage points (pp) and 6.2 pp. accuracy improvement over the base model for the nighttime and daytime videos, respectively. To speed up, the grayscale frame difference is used for the intermediate frames in a segment, which can double the processing speed.
Comments: to be published in International Conference on Image Processing (ICIP) 2022, Bordeaux, France
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2207.01183 [cs.CV]
  (or arXiv:2207.01183v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2207.01183

Submission history

From: Sandy Ardianto [view email]
[v1] Mon, 4 Jul 2022 03:55:19 UTC (1,288 KB)
[v2] Wed, 13 Jul 2022 15:04:18 UTC (1,672 KB)
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