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dc.contributor.authorTian, J.
dc.contributor.authorLu, Y.
dc.contributor.authorLi, L.
dc.contributor.authorLiu, Wan-Quan
dc.identifier.citationTian, J. and Lu, Y. and Li, L. and Liu, W. 2017. Tracking human poses in various scales with accurate appearance. International Journal of Machine Learning and Cybernetics. 8 (5): pp. 1667-1680.

Building a robust and fully automatic framework for human motion tracking in 2D images and videos remains a challenging task in computer vision due to cluttered backgrounds, self-occlusions, variations of body shape and complexities of human postures. In this paper we propose a robust framework for human motion tracking without motion priors. The proposed framework builds an accurate/uncontaminated specific appearance model and then tracks the target’s postures with this specific appearance model. The main contribution of this work is a novel process to build an accurate appearance model by identifying non-target pixels and removing them. In addition, for the goal of tracking in multiple scales, a novel strategy for scale evaluation and adjustment is proposed to adaptively change the scale values during the tracking process. Experiments show that the accurate specific appearance model outperforms existing work, and the proposed tracking system is able to successfully track challenging sequences with different appearances, motions, scales and angles of view.

dc.titleTracking human poses in various scales with accurate appearance
dc.typeJournal Article
dcterms.source.titleInternational Journal of Machine Learning and Cybernetics
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Science (EECMS)
curtin.accessStatusFulltext not available

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