Authors: Jalal A. Nasiri; Nasrollah Moghadam Charkari; Kourosh Mozafari
Addresses: Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran ' Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran ' Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Abstract: Hidden Markov model (HMM) has been widely applied in human action recognition. In this paper an extension of HMM called fuzzy hidden Markov model (fuzzy HMM) is used for action recognition. It tries to increase the classification performance and decrease the information loss due to feature vector quantisation. Using fuzzy concepts with HMM leads to better recognition of similar actions such as walking, jogging and running. Two feature extraction methods including skeleton and space-time approaches are used for action representation. Actions could be represented efficiently using skeleton features where scene background is plain. Space-time features are extracted directly from video, and therefore avoid possible failures of other pre-processing methods. We propose space-time-based features by considering temporal relation between them. Experimental results show the effectiveness of fuzzy HMM in human action recognition. Moreover, it is shown that fuzzy HMM leads to significant improvement in recognition of similar actions. The accuracy rates of fuzzy HMM in comparison to HMM are incremented 3.33% and 5.59% in Weizmann and KTH datasets respectively.
Keywords: human action recognition; fuzzy hidden Markov model; FHMM; skeleton features; space-time features.
International Journal of Computational Vision and Robotics, 2017 Vol.7 No.5, pp.538 - 557
Available online: 27 Jun 2017 *Full-text access for editors Access for subscribers Purchase this article Comment on this article