Abstract
The ubiquitous specular reflections arose from glasses seriously degrade accuracy of previous visual trackers. Although there have been few visual tracking schemes developed for mixed images with reflections, none focuses on the issue of multi-target tracking. Thus, this paper proposes a multi-target tracking scheme on mixed images with reflections. In the framework of particle filter, the proposed scheme combines the sample-based joint probabilistic data association filter (SJPDAF) with a single target based tracker that uses co-inference and maximum likelihood for visual cue integration to improve tracking accuracy of multiple targets. The co-inference predicted states are used for measurement validation of the SJPDAF. Experimental results show that the proposed scheme works well compared with the SJPDAF tracker.
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This work was supported by the Ministry of Science and Technology of Taiwan under the Grants MOST-103-2221-E-008-061 and MOST-104-2221-E-008-059.
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Zhang, Th., Chen, HT., Tang, CW. (2015). Multi-target Tracking Using Sample-Based Data Association for Mixed Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_12
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DOI: https://doi.org/10.1007/978-3-319-27857-5_12
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