Towards Silhouette-Aware Human Detection in Depth Images

Detecting humans in depth images attracts increasing attention thanks to the advantage of depth modality in privacy protection. However, this task is challenging because the number of available training data is limited to date and the depth images, unlike RGB images, are short of rich texture features. Although many image synthesis methods and deep learning methods have been proposed and proven successful, especially for RGB images, it is unsatisfactory to directly apply them to depth images because of the intrinsical differences between the modalities. In view of that silhouette of humans becomes an essential discriminative cue in depth images in the absence of texture information, which is not well utilized by existing methods, in this paper, we thus propose a silhouette-aware network (SAN) to train the detection model and a depth image synthesis method that represses spurious silhouette to augment the training data. Besides, to further increase the diversity of training data, we collect a dataset of scene depth images (SDI), including both indoor and outdoor scenes, as background images when synthesizing training data. Experimental results show that (i) our proposed synthesis method can generate more realistic depth images and thus benefits the training of detection models, (ii) our collected SDI dataset can effectively enhance data diversity and thus improves the effectiveness of the obtained detection models, and (iii) our proposed silhouette-aware network (SAN) can effectively boost the human detection accuracy. Our dataset is available at https://pan.baidu.com/s/13hpuziavBNjS8KATClpXww, password: r9id