NDLI: A spatiotemporal motion variation features extraction approach for human tracking and pose-based action recognition

Content Provider	IEEE Xplore Digital Library
Author	Jalal, A. Kamal, S. Farooq, A. Daijin Kim
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci. & Eng., POSTECH, Gyengbuk, South Korea (Jalal, A.; Daijin Kim) \|\| Depts. of Electron. & Biomed. Eng., Kyung Hee Univ., Yongin, South Korea (Kamal, S.; Farooq, A.)
Abstract	Video and image analysis technologies have made human action recognition as an interesting field and used in many practical application systems such as visual surveillance systems, healthcare monitoring systems, 3D games and smart home. In this paper, we address the challenges of automatic tracking, detection and recognition of three-dimensional human pose-based actions from sequences of depth maps. Specifically, we have designed our proposed idea into a probabilistic framework: (1) dividing each action into meaningful ordered temporal segments, (2) using pixel-neighboring intensity difference approach to identify human shape region from the confused scenes, (3) introducing a robust spatiotemporal features to extract 3D joints information via local distance features and depth human shape information to get motion variation features and (4) finally these sets of features are merged together and their feature vectors are further reduced and discriminated to get optimal vectors for real-world applications. Next, the augmented features are enhanced and symbolized by Linde-Buzo-Gray clustering algorithm for better action recognition. With these symbols, each action hidden Markov model (HMM) is trained and tested for action recognition. The experimental results demonstrated our proposed approach on three challenging depth video datasets: IM-DepthActions, MSRAction3D and MSRDailyActivity3D, producing significantly improved results over the state-of-the-art methods: especially for those actions that are not easily discernible by the existing methods.
Starting Page	1
Ending Page	6
File Size	465359
Page Count	6
File Format	PDF
e-ISBN	9781467369022
DOI	10.1109/ICIEV.2015.7334049
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-06-15
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Human shape region Action recognition Three-dimensional displays Shape Spatiotemporal features Hidden Markov models Hidden Markov model Feature extraction Spatiotemporal phenomena Fisher discriminant analysis Joints
Content Type	Text
Resource Type	Article

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