Authors:
Alireza Dehghani
and
Alistair Sutherland
Affiliation:
Dublin City University, Ireland
Keyword(s):
Interest Point Matching, Cyclic String Matching, Human Body Tracking.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
Abstract:
Current interest point (IP) matching algorithms are either local-based or spatial-based. We propose a hybrid local-spatial IP matching algorithm for articulated human body tracking. The first stage is local-based and finds matched pairs of IPs from two lists of reference and target IPs through a local-feature-descriptors-based matching method. The second stage of the algorithm is spatial-based. It starts with the confidently matched pairs of the previous stage, and recovers more matched pairs from the remaining unmatched IPs through graph matching and cyclic string matching. To compensate for the problem of Reference List Leakage (RLL), which decreases the number of reference IPs throughout the frame sequence and causes failure of tracking, an IP List Scoring and Refinement (LSR) strategy is proposed to maintain the number of reference IPs around a specific level. Experimental results show that not only the proposed algorithm increases the precision rate from 61.53% to 97.81%, but al
so it improves the recall rate from % 52.33 to 96.40%.
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