Abstract
Mobile visual search attracts an increasing attention in multimedia communication recent years which demands higher performance with less delay. An efficient moving object tracking algorithm based on the correlation filter framework is proposed in this paper aiming at accrute real-time mobile visual search in multimedia surveillance. We employ complementary features including HOG and color attributes for the target appearance representation, and the optimization to solve for both the kernelized correlation filter and the adaptive target response jointly is adopted to counter fast motion, motion blur and occlusion. Besides, a separate scale pyramid filter is applied for accurate scale estimation while being computationally efficient. Moreover, a compressed support vector machine (CSVM) serving as a modifier is employed with a compressed sensing matrix to further boost performance. Finally, extensive experimental results on large-scale benchmark datasets validate that the proposed algorithm outperforms state-of-the-art methods in terms of efficiency, accuracy, and robustness.
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Acknowledgments
This work was supported by NSFC (No. 61372069), “111” project (No. B08038) and the Fundamental Research Funds for the Central Universities.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, L., Xiao, S., Tan, F. (2018). Adaptive Long-Term Object Tracking for Real-Time Visual Search in Multimedia Surveillance. In: Li, B., Shu, L., Zeng, D. (eds) Communications and Networking. ChinaCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 236. Springer, Cham. https://doi.org/10.1007/978-3-319-78130-3_40
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DOI: https://doi.org/10.1007/978-3-319-78130-3_40
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