Sparse auto-encoder based feature learning for human body detection in depth image

Human body detection in depth image is an active research topic in computer vision. But depth feature extraction is still an open problem. In this paper, a novel feature learning method based on sparse auto-encoder (SAE) is proposed for human body detection in depth image. The proposed learning based feature enables capturing the intrinsic human body structure. To further reduce the computation cost of SAE, both convolution neural network and pooling are introduced to reduce the training complexity. In addition, upon learning SAE based depth feature, we further pursuit the detector efficiency. A beyond sliding window localization strategy is proposed based on the fact that the depth values of object surface are almost the same. The proposed localization strategy first uses the histogram of depth to generate candidate detection window center, and then exploits the relationship between human body height and depth to determine the detection window size. Thus, it can avoid the time-consuming sliding window search, and further enables fast human body localization. Experiments on SZU Depth Pedestrian dataset verify the effectiveness of our proposed method. HighlightsSparse auto-encoder is used to learn depth feature for human detection.A beyond sliding window localization method based on depth value.