【摘要】 针对步态识别中小面积人体遮挡问题,提出了一种基于Wasserstein GAN的图像补全网。该网络能够为图像中遮挡区域生成上下文一致的补全图像。为了减少噪声对特征提取的影响,采用具有鲁棒性的堆叠自动编码器进行特征提取。为了提高分类的能力,采用稀疏编码的方法对步态特征进行表示与分类。在公共数据集CASIA-B和TUM-GAID上对该方法进行了验证,并与其他方法进行了对比试验,结果表明了该方法的有效性。更多还原

【Abstract】 Aiming at the problem of small area human occlusion in gait recognition, a method based on generating adversarial image inpainting network was proposed which can generate a context consistent image for gait occlusion area. In order to reduce the effect of noise on feature extraction, the stacked automatic encoder with robustness was used. In order to improve the ability of gait classification, the sparse coding was used to express and classify the gait features. Experiments results showed the effectiveness of the proposed method in comparison with other state-of-the-art methods on the public databases CASIA-B and TUM-GAID for gait recognition.更多还原