Probabilistic color matching and tracking of human subjects
Applied Optics, Vol. 49, Issue 26, pp. 4926-4935 (2010)
http://dx.doi.org/10.1364/AO.49.004926
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Abstract
Pattern discovery algorithms based on the computational mechanics (CM) method have been shown to succinctly describe underlying patterns in data through the reconstruction of minimum probabilistic finite state automata (PFSA). We apply the CM approach toward the tracking of human subjects in real time by matching and tracking the underlying color pattern as observed from a fixed camera. Objects are extracted from a video sequence, and then raster scanned, decomposed with a one-dimensional Haar wavelet transform, and symbolized with the aid of a red–green–blue (RGB) color cube. The clustered causal state algorithm is then used to reconstruct the corresponding PFSA. Tracking is accomplished by generating the minimum PFSA for each subsequent frame, followed by matching the PFSAs to the previous frame. Results show that there is an optimum alphabet size and segmentation of the RGB color cube for efficient tracking.
© 2010 Optical Society of America
OCIS Codes
(100.0100) Image processing : Image processing
(150.1135) Machine vision : Algorithms
(100.4999) Image processing : Pattern recognition, target tracking
(150.6044) Machine vision : Smart cameras
ToC Category:
Image Processing
History
Original Manuscript: December 17, 2009
Revised Manuscript: August 6, 2010
Manuscript Accepted: August 10, 2010
Published: September 8, 2010
Virtual Issues
Vol. 5, Iss. 13 Virtual Journal for Biomedical Optics
Citation
Abdeq M. Abdi, Mendel Schmiedekamp, and Shashi Phoha, "Probabilistic color matching and tracking of human subjects," Appl. Opt. 49, 4926-4935 (2010)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-49-26-4926
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