Traffic police gesture recognition based on SlowFast network

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.