Research on Lightweight Traffic Police Gesture Recognition Based on Optical Flow Enhancement

ABSTRACT

Existing traffic police gesture recognition methods often rely on computationally intensive 3D CNNs or RNNs using skeletal data. To address these efficiency challenges during feature extraction, this study proposes a two‐stage recognition framework based on optical flow enhancement. First, the RAFT algorithm extracts the motion vectors of gesture targets, suppressing background interference from vehicles and pedestrians. Second, a lightweight ShuffleNetV2‐K5 architecture is designed as the feature extractor. By employing 5 × 5 depthwise separable convolutions, the network expands its receptive field to capture large‐scale spatial features, thereby reducing the required network depth. For temporal modelling, the Sparrow Search Algorithm (SSA) is introduced to dynamically optimise the hyperparameters of an LSTM network, which helps reduce prediction errors and mitigate overfitting. Experimental results demonstrate that this optical flow‐enhanced lightweight model balances recognition accuracy with computational efficiency, shortening training time while maintaining stability. Ultimately, this framework explores the efficacy of optical flow features and provides a theoretical foundation alongside a potential algorithmic approach for real‐time gesture recognition under favourable lighting conditions.