Feature Visualization Based Stacked Convolutional Neural Network for Human Body Detection in a Depth Image
Human body detection is a key technology in the fields of biometric recognition, and the detection in a depth image is rather challenging due to serious noise effects and lack of texture information. For addressing this issue, we propose the feature visualization based stacked convolutional neural network (FV-SCNN), which can be trained by a two-layer unsupervised learning. Specifically, the next CNN layer is obtained by optimizing a sparse auto-encoder (SAE) on the reconstructed visualization of the former to capture robust high-level features. Experiments on SZU Depth Pedestrian dataset verify that the proposed method can achieve favorable accuracy for body detection. The key of our method is that the CNN-based feature visualization actually pursues a data-driven processing for a depth map, and significantly alleviates the influences of noise and corruptions on body detection.
Human detection Depth image Feature visualization Sparse auto-encoder Convolutional neural network
Xiao Liu Ling Mei Dakun Yang Jianhuang Lai Xiaohua Xie
Sun Yat-sen University,Guangzhou 510006,China;Guangdong Key Laboratory of Information Security Technology,Guangzhou,China;Key Laboratory of Machine Intelligence and Advanced Computing,Ministry of Education,Guangzhou,China
国际会议
广州
英文
87-98
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）