CHD:Consecutive Horizontal Dropout for Human Gait Feature Extraction
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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 78.201 wearing coat or jacket condition. In addition, 100 was first obtained with CHD. On the benchmarks of CASIC-B, above accuracies are state-of-the-arts.
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