CCTV视频中快速错误方向骑行检测：稀疏采样足矣
Fast Wrong-way Cycling Detection in CCTV Videos: Sparse Sampling is All You Need
AI总结：
本文提出WWC-Predictor，通过稀疏采样和轻量级检测器高效估计错误方向骑行比例，实现低误差率和低计算成本。
摘要：
有效监控异常交通行为，如错误方向骑行（即逆向骑行自行车或电动自行车），对于优化执法部署和交通规划至关重要。然而，在资源受限的环境中，准确记录所有错误方向骑行事件既不必要也不可行，因为这需要高分辨率相机进行证据收集和事件检测。为了解决这一挑战，我们提出WWC-Predictor，一种新的方法，用于高效估计错误方向骑行比例，定义为给定时间段内错误方向骑行事件与总骑行运动数量的比例。我们方法的核心创新在于使用轻量级检测器在稀疏采样的帧中准确检测错误方向骑行事件，然后使用自回归移动平均模型估计总体比例。为了评估我们方法的有效性，我们构建了一个包含35分钟视频序列的基准数据集，具有分钟级注释。我们的方法实现了仅1.475%的平均误差率，同时仅消耗传统跟踪方法所需GPU时间的19.12%，验证了其在估计错误方向骑行比例方面的有效性。我们的源代码已公开在：https://github.com/VICA-Lab-HKUST-GZ/WWC-Predictor。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
