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arXiv · 2405.07293

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

Jing Xu·Wentao Shi·Sheng Ren·Lijuan Zhang·Weikai Yang·Pan Gao·Jie Qin
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Abstract

Effective monitoring of unusual transportation behaviors, such as wrong-way cycling (i.e., riding a bicycle or e-bike against designated traffic flow), is crucial for optimizing law enforcement deployment and traffic planning. However, accurately recording all wrong-way cycling events is both unnecessary and infeasible in resource-constrained environments, as it requires high-resolution cameras for evidence collection and event detection. To address this challenge, we propose WWC-Predictor, a novel method for efficiently estimating the wrong-way cycling ratio, defined as the proportion of wrong-way cycling events relative to the total number of cycling movements over a given time period. The core innovation of our method lies in accurately detecting wrong-way cycling events in sparsely sampled frames using a light-weight detector, then estimating the overall ratio using an autoregressive moving average model. To evaluate the effectiveness of our method, we construct a benchmark dataset consisting of 35 minutes of video sequences with minute-level annotations.Our method achieves an average error rate of a mere 1.475\% while consuming only 19.12\% GPU time required by conventional tracking methods, validating its effectiveness in estimating the wrong-way cycling ratio. Our source code is publicly available at: https://github.com/VICA-Lab-HKUST-GZ/WWC-Predictor.

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Jing Xu, Wentao Shi, Sheng Ren, Lijuan Zhang, Weikai Yang, Pan Gao, Jie Qin. 2025-12-15. Fast Wrong-way Cycling Detection in CCTV Videos: Sparse Sampling is All You Need. https://arxiv.org/abs/2405.07293

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