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- Title
Sequential Markov random fields for human body parts tracking.
- Authors
Cao, Xiao-Qin; Liu, Zhi-Qiang
- Abstract
This paper presents a novel human body part tracker called sequential Markov random fields (SMRFs), which can be used to extract spatiotemporal features in human action recognition. Given a video sequence of human action in the monocular settings, SMRF can effectively detect the key spatiotemporal feature points on human body parts. We also develop efficient learning algorithms for the SMRF tracker using relaxation labeling (RL). Our results show that the SMRF tracker performs better than some state-of-the-art trackers for human action recognition.
- Subjects
IMAGE recognition (Computer vision); MARKOV processes; MARKOV random fields; SPATIOTEMPORAL processes; MOTION analysis
- Publication
Multimedia Tools & Applications, 2015, Vol 74, Issue 17, p6671
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-014-1924-3