Real-time and robust multiple-view gender classification using gait features in video surveillance
This addresses the need for robust, real-time gender classification in surveillance, but it is incremental as it builds on existing gait-based methods with specific improvements.
The paper tackled the problem of gender classification in video surveillance under challenging conditions like arbitrary walking directions and carried items, achieving 98.8% accuracy on the CASIA Dataset B and outperforming state-of-the-art methods.
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.