Real-Time Salient Closed Boundary Tracking using Perceptual Grouping and Shape Priors

Xuebin Qin, Shida He, Zichen Zhang, Masood Dehghan and Martin Jagersand

Abstract

In this paper, we propose a real-time method for accurate salient closed boundary tracking via a combination of shape constraints and perceptual grouping on edge fragments. Particularly, we encode the Gestalt law of proximity and the prior shape constraint in a novel ratio-form grouping cost. The proximity and prior constraint are depicted by the relative gap length and average distance difference along the to-be-tracked boundary with respect to its area. We build a graph using the detected edge fragments and in-between gaps. The grouping problem is formulated as searching for a special cycle in this graph with a minimum grouping cost. To reduce the search space and achieve real-time performance, we propose a set of novel techniques for efficient edge fragments splitting and filtering. We evaluate this method on a public real-world video dataset against other methods.

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DOI

10.5244/C.31.12
https://dx.doi.org/10.5244/C.31.12

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Bibtex

            @inproceedings{BMVC2017_12,
                title={Real-Time Salient Closed Boundary Tracking using Perceptual Grouping and Shape Priors},
                author={Xuebin Qin, Shida He, Zichen Zhang, Masood Dehghan and Martin Jagersand},
                year={2017},
                month={September},
                pages={12.1-12.11},
                articleno={12},
                numpages={11},
                booktitle={Proceedings of the British Machine Vision Conference (BMVC)},
                publisher={BMVA Press},
                editor={Tae-Kyun Kim, Stefanos Zafeiriou, Gabriel Brostow and Krystian Mikolajczyk},
                doi={10.5244/C.31.12},
                isbn={1-901725-60-X},
                url={https://dx.doi.org/10.5244/C.31.12}
            }