CAD: concatenated action descriptor for one and two person(s), using silhouette and silhouette's skeleton. Issue 3 (30th January 2020)

• Record Type: Journal Article
• Authors: Islam, M. Shujah; Iqbal, Mansoor; Naqvi, Nuzhat; Bakhat, Khush; Islam, M. Mattah; Kanwal, Shamsa; Ye, Zhongfu
• Is Part Of: IET image processing. Volume 14:Issue 3(2020)
• Page Start: 417
• Page End: 422
• Publication Date: 2020-01-30
• Subjects: support vector machines -- image sequences -- image motion analysis -- object recognition -- image classification -- object detection; CAD -- concatenated action descriptor -- human action recognition -- signature‐based corner points -- silhouette skeleton -- keypoint detection -- signature‐based optical flow -- binary robust invariant scalable keypoints; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2018.6437
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : This study introduces an action descriptor that has the ability to perform human action recognition efficiently for one and two person(s). The authors' proposed descriptor computes information like motion, spatial–temporal, diversion with respect to the centroid, critical point and keypoint detection, whereas the existing approaches lack to address this information efficiently. Action descriptors are developed from signature‐based optical flow, signature‐based corner points and binary robust invariant scalable keypoints. These action descriptors are applied to silhouette and silhouette's skeleton frames. These aforementioned action descriptors lead to developing the concatenated action descriptor (CAD). In order to develop action descriptors, the reference video frame plays an important role. Weizmann (one person) and both clean and noise versions of SBU Kinect Interaction (two persons) datasets are used for the evaluation of their proposed descriptors. On the other hand, classifications are performed by using support vector machine. Experimental results demonstrate that CAD not only outperforms among the entire proposed descriptors, but also provides better performance as compared to state‐of‐the‐art approaches.