Abstract
Generative and discriminative models are commonly used in object tracking algorithms. However, the limitation of using these models lies in the fact proved by a large number of experiments that a single model is easily influenced by external factors, such as occlusion and illumination variation. To address this issue, in this paper based on a collaborative model within the cascaded feedback framework, we propose an online object tracking algorithm where an adaptive generative model has been developed which can adapt to the dynamic background. Experimentally, we show that our algorithm is able to outperform the state-of-the-art trackers on the various benchmark videos.
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Tian, S., Wei, Z. (2014). Online Object Tracking via Collaborative Model within the Cascaded Feedback Framework. In: Miao, D., Pedrycz, W., Ślȩzak, D., Peters, G., Hu, Q., Wang, R. (eds) Rough Sets and Knowledge Technology. RSKT 2014. Lecture Notes in Computer Science(), vol 8818. Springer, Cham. https://doi.org/10.1007/978-3-319-11740-9_29
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DOI: https://doi.org/10.1007/978-3-319-11740-9_29
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