Title: Semi-supervised learning of pose-specific detector for human lying-pose detection

Authors: Daoxun Xia; Haojie Liu; Fang Guo; Weian Li

Addresses: School of Big Data and Computer Science, Guizhou Normal University, Guiyang, Guizhou, China ' School of Big Data and Computer Science, Guizhou Normal University, Guiyang, Guizhou, China ' School of Big Data and Computer Science, Guizhou Normal University, Guiyang, Guizhou, China ' School of Big Data and Computer Science, Guizhou Normal University, Guiyang, Guizhou, China

Abstract: Under the superlow-altitude aerial image, human lying-pose detection is an important problem in object detection. This paper is mainly focused on the application study of an Unmanned Aerial Vehicle (UAV) life detector after a disaster, and we study the problem of learning an effective pose-specific detector using weakly annotated images and a deep neural network. This typical approach (1) clusters a series of human poses for the human lying-pose and assigns an image-level label to all human lying-poses in each image and breaks them down into several categories; (2) trains multiple classifiers for each category using a deep neural network; and (3) uses the boosted semi-supervised CNN forest classifier to select a human lying-pose with high confidence scores as the positive instances for another round of training. Experiments on the XiaMen University Lying-Pose Dataset (XMULP) show that significant performance improvement can be achieved with our proposed method.

Keywords: human lying-pose detection; pose-specific detector; semi-supervised learning; object detection.

DOI: 10.1504/IJWMC.2021.117549

International Journal of Wireless and Mobile Computing, 2021 Vol.20 No.4, pp.320 - 327

Received: 20 Aug 2020
Accepted: 14 Sep 2020
Published online: 13 Sep 2021 *

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