CHD:Consecutive Horizontal Dropout for Human Gait Feature Extraction

TL;DR

This paper introduces the Consecutive Horizontal Dropout (CHD) method to improve deep learning-based human gait feature extraction, significantly enhancing recognition accuracy especially in challenging conditions like carrying bags or wearing coats.

Contribution

The paper proposes a novel CHD technique that reduces overfitting in deep networks, boosting gait recognition and person re-identification accuracy in various challenging scenarios.

Findings

10% increase in cross-view gait recognition accuracy

8% improvement in person re-identification accuracy with coats

Achieved 100% accuracy in NM condition on CASIA-B

Abstract

Despite gait recognition and person re-identification researches have made a lot of progress, the accuracy of identification is not high enough in some specific situations, for example, people carrying bags or changing coats. In order to alleviate above situations, we propose a simple but effective Consecutive Horizontal Dropout (CHD) method apply on human feature extraction in deep learning network to avoid overfitting. Within the CHD, we intensify the robust of deep learning network for cross-view gait recognition and person re-identification. The experiments illustrate that the rank-1 accuracy on cross-view gait recognition task has been increased about 10% from 68.0% to 78.201% and 8% from 83.545% to 91.364% in person re-identification task in wearing coat or jacket condition. In addition, 100% accuracy of NM condition was first obtained with CHD. On the benchmarks of CASIA-B, above accuracies are state-of-the-arts.