Deep-learning-based algorithm for classifying pedestrian behavior at crosswalks

Menghao Li; Miao Liu

doi:10.1117/12.3050750

24 October 2024 Deep-learning-based algorithm for classifying pedestrian behavior at crosswalks

Menghao Li, Miao Liu

Proceedings Volume 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024); 133960H (2024) https://doi.org/10.1117/12.3050750
Event: 3rd International Conference on Image Processing, Object Detection and Tracking (IPODT24), 2024, Nanjing, China

Abstract

In intelligent transportation systems, pedestrian intentions at intersections must be classified to enhance pedestrian safety and optimize traffic flow. Accurately predicting pedestrian actions can significantly reduce traffic accidents and improve overall traffic management efficiency. An improved YOLOv5 model is proposed to efficiently and accurately identify pedestrian intentions. The model combines the Multi-scale Diluted Attention (MSDA) mechanism, which extends attention to different scales to capture the subtle movements of pedestrians, and the GhostNet lightweight module, which reduces the number of computational parameters and makes the model suitable for real-time applications. In addition, a new loss function, ACFloss, which combines adaptive weight-focusing loss based on contextual relationships and spatio-temporal consistency loss, is designed to understand the behavioral intent of actions better. Experimental results show that the improved YOLOv5 model achieves a classification accuracy of 93.7%, 4.5% higher than the benchmark, and the inference speed is improved by 35%. This improvement provides a reliable solution for intelligent transportation systems in complex intersection scenarios.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Menghao Li and Miao Liu "Deep-learning-based algorithm for classifying pedestrian behavior at crosswalks", Proc. SPIE 13396, Third International Conference on Image Processing, Object Detection, and Tracking (IPODT 2024), 133960H (24 October 2024); https://doi.org/10.1117/12.3050750

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