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dc.contributor.authorTian, J.
dc.contributor.authorLi, Ling
dc.contributor.authorLiu, Wan-Quan
dc.identifier.citationTian, J. and Li, L. and Liu, W. 2014. Multi-Scale Human Pose Tracking in 2D Monocular Images. Journal of Computer and Communications. 2: pp. 78-84.

In this paper we address the problem of tracking human poses in multiple perspective scales in 2D monocular images/videos. In most state-of-the-art 2D tracking approaches, the issue of scale variation is rarely discussed. However in reality, videos often contain human motion with dynamically changed scales. In this paper we pro-pose a tracking framework that can deal with this problem. A scale checking and adjusting algorithm is pro-posed to automatically adjust the perspective scales during the tracking process. Two metrics are proposed for detecting and adjusting the scale change. One metric is from the height value of the tracked target, which is suitable for some sequences where the tracked target is upright and with no limbs stretching. The other metric employed in this algorithm is more generic, which is invariant to motion types. It is the ratio between the pixel counts of the target silhouette and the detected bounding boxes of the target body. The proposed algorithm is tested on the publicly available datasets (HumanEva). The experimental results show that our method demon-strated higher accuracy and efficiency compared to state-of-the-art approaches

dc.publisherScientific Research Publishing, Inc
dc.subjectHuman Motion Tracking
dc.subjectMonocular Video
dc.titleMulti-Scale Human Pose Tracking in 2D Monocular Images
dc.typeJournal Article
dcterms.source.titleJournal of Computer and Communications

This open access article is distributed under the Creative Commons license

curtin.departmentDepartment of Computing
curtin.accessStatusOpen access

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