CHD:Consecutive Horizontal Dropout for Human Gait Feature Extraction
This work addresses accuracy issues in gait recognition and person re-identification for scenarios with clothing variations, representing an incremental improvement.
The paper tackled the problem of low accuracy in gait recognition and person re-identification in specific situations like people carrying bags or changing coats, by proposing a Consecutive Horizontal Dropout (CHD) method for feature extraction, resulting in a rank-1 accuracy increase of about 10% from 68.0% to 78.201% in cross-view gait recognition and 8% from 83.545% to 91.364% in person re-identification under coat conditions, achieving state-of-the-art results on the CASIA-B benchmark.
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.