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Cited 65 time in webofscience Cited 84 time in scopus
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Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM SCIE SCOPUS KCI

Title
Depth Images-based Human Detection, Tracking and Activity Recognition Using Spatiotemporal Features and Modified HMM
Authors
Kamal, SJalal, AKim, D
Date Issued
2016-11
Publisher
대한전기학회
Abstract
Human activity recognition using depth information is an emerging and challenging technology in computer vision due to its considerable attention by many practical applications such as smart home/office system, personal health care and 3D video games. This paper presents a novel framework of 3D human body detection, tracking and recognition from depth video sequences using spatiotemporal features and modified HMM. To detect human silhouette, raw depth data is examined to extract human silhouette by considering spatial continuity and constraints of human motion information. While, frame differentiation is used to track human movements. Features extraction mechanism consists of spatial depth shape features and temporal joints features are used to improve classification performance. Both of'these features are fused together to recognize different activities using the modified hidden Markov model (M-HMM). The proposed approach is evaluated on two challenging depth video datasets. Moreover, our system has significant abilities to handle subject's body parts rotation and body parts missing which provide major contributions in human activity recognition.
URI
https://oasis.postech.ac.kr/handle/2014.oak/37237
DOI
10.5370/JEET.2016.11.6.1857
ISSN
1975-0102
Article Type
Article
Citation
Journal of Electrical Engineering & Technology, vol. 11, no. 6, page. 1857 - 1862, 2016-11
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김대진KIM, DAI JIN
Dept of Computer Science & Enginrg
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