Sparse Sampling is All You Need for Fast Wrong-way Cycling Detection in CCTV Videos

• URL: http://arxiv.org/abs/2405.07293v1
• Date: Sun, 12 May 2024 14:16:05 GMT
• Title: Sparse Sampling is All You Need for Fast Wrong-way Cycling Detection in CCTV Videos
• Authors: Jing Xu, Wentao Shi, Sheng Ren, Pan Gao, Peng Zhou, Jie Qin,
• Abstract summary: This paper formulates a problem of detecting wrong-way cycling ratios in CCTV videos. We propose a sparse sampling method called WWC-Predictor to efficiently solve this problem. Our approach achieves an average error rate of a mere 1.475% while taking only 19.12% GPU time.
• Score: 36.1376919510996
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the field of transportation, it is of paramount importance to address and mitigate illegal actions committed by both motor and non-motor vehicles. Among those actions, wrong-way cycling (i.e., riding a bicycle or e-bike in the opposite direction of the designated traffic flow) poses significant risks to both cyclists and other road users. To this end, this paper formulates a problem of detecting wrong-way cycling ratios in CCTV videos. Specifically, we propose a sparse sampling method called WWC-Predictor to efficiently solve this problem, addressing the inefficiencies of direct tracking methods. Our approach leverages both detection-based information, which utilizes the information from bounding boxes, and orientation-based information, which provides insights into the image itself, to enhance instantaneous information capture capability. On our proposed benchmark dataset consisting of 35 minutes of video sequences and minute-level annotation, our method achieves an average error rate of a mere 1.475% while taking only 19.12% GPU time of straightforward tracking methods under the same detection model. This remarkable performance demonstrates the effectiveness of our approach in identifying and predicting instances of wrong-way cycling.

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