Anonymization of Human Gait in Video Based on Silhouette Deformation and Texture Transfer

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.