Joint motion model for local stereo video-matching method

Jinglin Zhang; Cong Bai; Jean Francois Nezan; Jean-Gabriel Cousin

doi:10.1117/1.OE.54.12.123108

21 December 2015 Joint motion model for local stereo video-matching method

Jinglin Zhang, Cong Bai, Jean Francois Nezan, Jean-Gabriel Cousin

Author Affiliations +

Optical Engineering, Vol. 54, Issue 12, 123108 (December 2015). https://doi.org/10.1117/1.OE.54.12.123108

Abstract

As one branch of stereo matching, video stereo matching becomes more and more significant in computer vision applications. The conventional stereo matching methods for static images would cause flicker-frames and worse matching results. We propose a joint motion-based square step (JMSS) method for stereo video matching. The motion vector is introduced as one component in the support region building for the raw cost aggregation. Then we aggregate the raw cost along two directions in the support region. Finally, the winner-take-all strategy determines the best disparity under our hypothesis. Experimental results show that the JMSS method not only outperforms other state-of-the-art stereo matching methods on test sequences with abundant movements, but also performs well in some real-world scenes with fixed and moving stereo cameras, respectively, in particular under some extreme conditions of real stereo visions. Additionally, the proposed JMSS method can be implemented in real time, which is superior to other state-of-the-art methods. The time efficiency is also a very important consideration in our algorithm design.

Citation Download Citation

Jinglin Zhang, Cong Bai, Jean Francois Nezan, and Jean-Gabriel Cousin "Joint motion model for local stereo video-matching method," Optical Engineering 54(12), 123108 (21 December 2015). https://doi.org/10.1117/1.OE.54.12.123108

Published: 21 December 2015

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