Abstract
Recently, compressive sensing has been widely used in the field of object tracking. As a typical representative, Compressive Tracking (CT) outperforms the state-of-the-art approaches. But it has a drawback that the scale of object is invariable throughout the whole tracking process. For solving this problem and further improve its performance, in this paper we propose a Weakly Supervised Compressive Tracking (WSCT) approach. Firstly, we introduce an effective prediction model based on optical flow, which could achieve reliable estimation of the position and the scale of current object. Secondly, through the prediction model, the samples around the predicted position of the current frame could be further utilized as weakly supervised information to guide the compressive classifier’s updating. In this way, this updating strategy combines the current information and future information to alleviate the phenomenon of object drift. Finally, the updated classifiers are used to locate the final object position. Our WSCT algorithm shows robustness for larger scale changes, especially in film and television videos. Experiments and comparisons on challenging tracking sequences have shown the effectiveness of our approach.
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Liang, Y., Yin, XC., Tian, S., Hao, HW. (2013). Weakly Supervised Compressive Tracking with Effective Prediction Model. In: Huet, B., Ngo, CW., Tang, J., Zhou, ZH., Hauptmann, A.G., Yan, S. (eds) Advances in Multimedia Information Processing – PCM 2013. PCM 2013. Lecture Notes in Computer Science, vol 8294. Springer, Cham. https://doi.org/10.1007/978-3-319-03731-8_74
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DOI: https://doi.org/10.1007/978-3-319-03731-8_74
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