Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/138068
Title: Pose-invariant kinematic features for action recognition
Authors: Ramanathan, Manoj
Yau, Wei-Yun
Teoh, Eam Khwang
Thalmann, Nadia Magnenat
Keywords: Engineering::Computer science and engineering
Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Ramanathan, M., Yau, W.-Y., Teoh, E. K., & Thalmann, N. M. (2017). Pose-invariant kinematic features for action recognition. Proceedings of the 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), 292-299. doi:10.1109/APSIPA.2017.8282038
Abstract: Recognition of actions from videos is a difficult task due to several factors like dynamic backgrounds, occlusion, pose-variations observed. To tackle the pose variation problem, we propose a simple method based on a novel set of pose-invariant kinematic features which are encoded in a human body centric space. The proposed framework begins with detection of neck point, which will serve as a origin of body centric space. We propose a deep learning based classifier to detect neck point based on the output of fully connected network layer. With the help of the detected neck, propagation mechanism is proposed to divide the foreground region into head, torso and leg grids. The motion observed in each of these body part grids are represented using a set of pose-invariant kinematic features. These features represent motion of foreground or body region with respect to the detected neck point's motion and encoded based on view in a human body centric space. Based on these features, poseinvariant action recognition can be achieved. Due to the body centric space is used, non-upright human posture actions can also be handled easily. To test its effectiveness in non-upright human postures in actions, a new dataset is introduced with 8 non-upright actions performed by 35 subjects in 3 different views. Experiments have been conducted on benchmark and newly proposed non-upright action dataset to identify limitations and get insights on the proposed framework.
URI: https://hdl.handle.net/10356/138068
ISBN: 9781538615430
DOI: 10.1109/APSIPA.2017.8282038
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/APSIPA.2017.8282038
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:IMI Conference Papers

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