These days, a lot of videos are uploaded onto web-based video sharing services such as YouTube. These videos can be freely accessed from all over the world. On the other hand, they often contain the appearance of walking private people, which could be identified by silhouette-based gait recognition techniques rapidly developed in recent years. This causes a serious privacy issue. To avoid it, this paper proposes a method for anonymizing the appearance of walking people, namely human gait, in video. In the proposed method, we first crop human regions from all frames in an input video and binarize them to get their silhouettes. Next, we slightly deform the silhouettes from the aspects of static body shape and dynamic walking rhythm so that the person in the input video cannot be correctly identified by gait recognition techniques. After that, the textures of the original human regions are transferred onto the deformed silhouettes. We achieve this by a displacement field-based approach, which is training-free and thus robust to a variety of clothes. Finally, the anonymized human regions with the transferred textures are filled back into the input video. In the results of our experiments, we successfully degraded the accuracy of CNN-based gait recognition systems from 100% to 1.57% in the lowest case without yielding serious distortion in the appearance of the human regions, which demonstrated the effectiveness of the proposed method.

中文翻译：

---

基于轮廓变形和纹理迁移的视频中人体步态匿名化


如今，大量视频被上传到 YouTube 等基于网络的视频共享服务。这些视频可以从世界各地免费访问。另一方面，它们通常包含行走的私人外观，可以通过近年来迅速发展的基于轮廓的步态识别技术来识别。这会导致严重的隐私问题。为了避免这种情况，本文提出了一种对视频中行走的人的外观（即人类步态）进行匿名化的方法。在所提出的方法中，我们首先从输入视频中的所有帧中裁剪人体区域并将其二值化以获得其轮廓。接下来，我们从静态体形和动态行走节奏方面对轮廓进行轻微变形，使得步态识别技术无法正确识别输入视频中的人。之后，原始人体区域的纹理被转移到变形的轮廓上。我们通过基于位移场的方法来实现这一目标，该方法无需培训，因此对各种服装都具有鲁棒性。最后，带有传输纹理的匿名人体区域被填充回输入视频中。在我们的实验结果中，我们成功地将基于 CNN 的步态识别系统的准确率在最低情况下从 100% 降低到 1.57%，而没有对人体区域的外观产生严重的扭曲，这证明了该方法的有效性。