NDLI: CAD: concatenated action descriptor for one and two person(s), using silhouette and silhouette's skeleton

Content Provider	IET Digital Library
Author	Islam, M. Shujah Iqbal, Mansoor Naqvi, Nuzhat Bakhat, Khush Islam, M. Mattah Kanwal, Shamsa Ye, Zhongfu
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.
Starting Page	417
Ending Page	422
Page Count	6
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 3, Feb (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.6437
Journal	IET Image Processing
Publisher Date	2019-10-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Binary Robust Invariant Scalable Keypoints CAD Computer Vision And Image Processing Technique Concatenated Action Descriptor Human Action Recognition Image Classification Image Motion Analysis Image Recognition Image Sequence Keypoint Detection Knowledge Engineering Technique Object Detection Object Recognition Signature-based Corner Point Signature-based Optical Flow Silhouette Skeleton Support Vector Machine
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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