Continuous‐dilated temporal and inter‐frame motion excitation feature learning for gait recognition

Abstract

AbstractThe authors present global‐interval and local‐continuous feature extraction networks for gait recognition. Unlike conventional gait recognition methods focussing on the full gait cycle, the authors introduce a novel global‐ continuous‐dilated temporal feature extraction (TFE) to extract continuous and interval motion features from the silhouette frames globally. Simultaneously, an inter‐frame motion excitation (IME) module is proposed to enhance the unique motion expression of an individual, which remains unchanged regardless of clothing variations. The spatio‐temporal features extracted from the TFE and IME modules are then weighted and concatenated by an adaptive aggregator network for recognition. Through the experiments over CASIA‐B and mini‐OUMVLP datasets, the proposed method has shown the comparable performance (as 98%, 95%, and 84.9% in the normal walking, carrying a bag or packbag, and wearing coats or jackets categories in CASIA‐B, and 89% in mini‐OUMVLP) to the other state‐of‐the‐art approaches. Extensive experiments conducted on the CASIA‐B and mini‐OUMVLP datasets have demonstrated the comparable performance of our proposed method compared to other state‐of‐the‐art approaches.