Abstract
A real-time object tracking algorithm is presented based on the on-line support vector machine (SVM) scheme. A new training framework is proposed, which enables us to select reliable training samples from the image sequence for tracking. Multiple candidate regeneration, a statistical method, is employed to decrease the computational cost, and a directional-edge-based feature representation algorithm is used to represent images robustly as well as compactly. The structure of the algorithm is designed especially for real-time performance, which can extend the advantages of SVM to most of the general tracking applications. The algorithm has been evaluated on challenging video sequences and showed robust tracking ability with accurate tracking results. The hardware implementation is also discussed, while verification has been done to prove the real-time ability of this algorithm.
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References
Avidan, S.: Support Vector Tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 1064–1072 (2004)
Tang, F., Brennan, S., Zhao, Q., Tao, H.: Co-Tracking Using Semi-Supervised Support Vector Machines. In: IEEE 11th International Conference on Computer Vision, pp. 1–8 (2007)
Tian, M., Zhang, W., Liu, F.: On-Line Ensemble SVM for Robust Object Tracking. In: Yagi, Y., Kang, S., Kweon, I., Zha, H. (eds.) ACCV 2007, Part I. LNCS, vol. 4843, pp. 355–364. Springer, Heidelberg (2007)
Matthews, L., Ishikawa, T., Baker, S.: The Template Update Problem. IEEE Transactions on Pattern Analysis and Machine Intelligence 26, 810–815 (2004)
Syed, N., Liu, H., Sung, K.: Incremental Learning with Support Vector Machines. In: Proceedings of the Workshop on Support Vector Machines at the International Joint Conference on Articial Intelligence, Stockholm, Sweden (1999)
Cauwenberghs, G., Poggio, T.: Incremental and Decremental Support Vector Machine Learning. In: Advances in Neural Information Processing Systems, vol. 13 (2001)
Ruping, S.: Incremental Learning with Support Vector Machines. In: Proceedings IEEE International Conference on Data Mining, San Jose, CA, USA, pp. 641–642 (2001)
Zhu, H., Zhao, P., Shibata, T.: Directional-edge-based Object Tracking Employing On-line Learning and Regeneration of Multiple Candidate Locations. In: Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 2630–2633 (2010)
Babenko, B., Yang, M., Belongie, S.: Robust Object Tracking with Online Multiple Instance Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 1619–1632 (2011)
Zhang, R., Shibata, T.: A Fully-Parallel Self-Learning Analog Support Vector Machine Employing Compact Gaussian-Generation Circuits. In: International Conference on Solid State Devices and Materials (SSDM), Nagoya, pp. 174–175 (2011)
Suzuki, Y., Shibata, T.: Multiple-clue Face Detection Algorithm Using Edge-Based Feature Vectors. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 5, pp. V-737–V-740 (2004)
Zhu, H., Shibata, T.: A Real-Time Image Recognition System Using a Global Directional-Edge-Feature Extraction VLSI Processor. In: Proceedings of ESSCIRC, pp. 248–251 (2009)
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Zhao, P., Zhang, R., Shibata, T. (2012). Real-Time Object Tracking Algorithm Employing On-Line Support Vector Machine and Multiple Candidate Regeneration. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_72
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DOI: https://doi.org/10.1007/978-3-642-29347-4_72
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