Semi-supervised learning of pose-specific detector for human lying-pose detection Online publication date: Mon, 13-Sep-2021
by Daoxun Xia; Haojie Liu; Fang Guo; Weian Li
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 20, No. 4, 2021
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.
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