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

TL;DR

This paper introduces WWC-Predictor, a lightweight and efficient method for estimating the ratio of wrong-way cycling in CCTV videos using sparse sampling and autoregressive modeling, achieving high accuracy with minimal resource use.

Contribution

The paper presents a novel approach combining sparse frame sampling, a lightweight detector, and autoregressive modeling to accurately estimate wrong-way cycling ratios efficiently.

Findings

Achieves 1.475% average error rate in estimation.

Uses only 19.12% of GPU time compared to traditional methods.

Constructed a new benchmark dataset with 35 minutes of annotated videos.

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.