Abstract
Sensitivity to scene such as contrast and illumination intensity, is one of the factors significantly affecting the performance of object trackers. In order to overcome this issue, tracker parameters need to be adapted based on changes in contextual information. In this paper, we propose an intelligent mechanism to adapt the tracker parameters, in a real-time and online fashion. When a frame is processed by the tracker, a controller extracts the contextual information, based on which it adapts the tracker parameters for successive frames. The proposed controller relies on a learned neuro-fuzzy inference system to find satisfactory tracker parameter values. The proposed approach is trained on nine publicly available benchmark video data sets and tested on three unrelated video data sets. The performance comparison indicates clear tracking performance improvement in comparison to tracker with static parameter values, as well as other state-of-the art trackers.
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Acknowledgments
This work is supported by The Panorama and Centaur European projects as well as The Movement French project.
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Chau, D.P., Subramanian, K., Brémond, F. (2015). Adaptive Neuro-Fuzzy Controller for Multi-object Tracker. In: Nalpantidis, L., Krüger, V., Eklundh, JO., Gasteratos, A. (eds) Computer Vision Systems. ICVS 2015. Lecture Notes in Computer Science(), vol 9163. Springer, Cham. https://doi.org/10.1007/978-3-319-20904-3_42
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DOI: https://doi.org/10.1007/978-3-319-20904-3_42
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