Real-time and robust multiple-view gender classification using gait features in video surveillance

TL;DR

This paper introduces a real-time, view-robust gait-based gender classification method using average gait images and view-dependent classifiers, achieving high accuracy and computational efficiency in video surveillance.

Contribution

It proposes a novel, efficient approach combining AGI, viewpoint modeling, and silhouette cleaning to improve gender classification robustness in arbitrary viewing conditions.

Findings

Achieves 98.8% accuracy on CASIA Dataset B

Outperforms recent state-of-the-art methods

Robust against view changes and clothing variations

Abstract

It is common to view people in real applications walking in arbitrary directions, holding items, or wearing heavy coats. These factors are challenges in gait-based application methods because they significantly change a person's appearance. This paper proposes a novel method for classifying human gender in real time using gait information. The use of an average gait image (AGI), rather than a gait energy image (GEI), allows this method to be computationally efficient and robust against view changes. A viewpoint (VP) model is created for automatically determining the viewing angle during the testing phase. A distance signal (DS) model is constructed to remove any areas with an attachment (carried items, worn coats) from a silhouette to reduce the interference in the resulting classification. Finally, the human gender is classified using multiple view-dependent classifiers trained using a support vector machine. Experiment results confirm that the proposed method achieves a high accuracy of 98.8% on the CASIA Dataset B and outperforms the recent state-of-the-art methods.