Traffic police gesture recognition based on SlowFast network

Xinting Zhu; Ming Fang

doi:10.1117/12.3048304

3 October 2024 Traffic police gesture recognition based on SlowFast network

Xinting Zhu, Ming Fang

Author Affiliations +

Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 1327230 (2024) https://doi.org/10.1117/12.3048304
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China

Abstract

As the field of self-driving cars accelerates, the accurate interpretation of traffic officers' signals has become a pivotal technological advancement. Current research has explored various feature representation-based methods for traffic police gesture recognition, including RGB frames, optical flow, human skeletons, and point clouds. However, the existing methods still face challenges in spatio-temporal information fusion and feature extraction. Specifically, the skeletonbased method, noted for its focus on actions and compactness, could benefit from improvements in processing speed and robustness. In response to these challenges, the present study proposes a SlowFast network-based approach for traffic police gesture recognition (SF-TPGR). The method uses 3D heatmaps as inputs to convey the relative positional information of joint points through heatmap intensity changes, thereby improving model accuracy and speed by reducing the redundancy of heatmaps for lower limb joints. The SlowFast network architecture is then used for traffic police gesture recognition, effectively modeling rapidly changing actions such as going straight and lane changing with high frame refresh rates. The comparative experiment conducted on the Chinese traffic police gesture dataset shows that the accuracy of this method reaches 98.10%, and the inference time is reduced to 79 ms. Although its accuracy may be slightly lower than some top-performing methods in certain cases, its capability to rapidly extract human movements is a significant advantage.

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

Citation Download Citation

Xinting Zhu and Ming Fang "Traffic police gesture recognition based on SlowFast network", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 1327230 (3 October 2024); https://doi.org/10.1117/12.3048304

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