Head-heuristic human detection in RGB-D images
Xi En Cheng, Yi Cheng Li, Yong Kang Peng · 2018
Reliable human detection and tracking is important for a wide range of applications. In this paper, a particular designed method for real-time human detection has been proposed. The method is robustly in cluttered and dynamic environments, and deals with depth images. The method has two steps, first the hypothesis human head regions are localized by a superpixel based segmentation and merging approach. Then we utilize a multi-channel measurement and employ neural network for classification between human and non-human region refinement. Our approach, which detects human in depth images, allows very fast speed and high accuracy in three publicly available datasets.