Abstract
Multiple objects tracking in dynamic background is one of the key techniques in computer vision. An improved method of multiple objects tracking based on a combination of Camshift and object trajectory tracking is presented in this paper. The algorithm uses Harris corner matching to estimate background movement parameters, adopts two-frame difference to detect moving objects, combines object trajectory tracking with Camshift track moving objects. Our improved algorithm can achieve satisfactory effect not only in tracking multiple objects, but also in tracking continuously the objects which are static, re-enter the current scene or recover motion. The experiments show that the improved algorithm can achieve better result in the accuracy and robustness of detecting and tracking moving objects for dynamic background.
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References
Ristic, B., Arulampalam, S., Gordon, N.: Beyond the Kalman Filter: Particle Filters for Tracking Applications. Artech House, Boston (2004)
Moussakhani, B., Flam, J.T., Ramstad, T.A., Balasingham, I.: On change detection in a Kalman filter based tracking problem. Signal Processing 105, 268–276 (2014)
Dorin, C., Visvanathan, R., Peter, M.: Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(5), 564–577 (2003)
Hong-zhi, Z., Jin-huan, Z., Hui, Y., Shi-lin, H.: Object Tracking Algorithm Based on CamShift. Computer Engineering and Design 27(11), 108–110 (2006)
Nummiaro, K., Koller-Meier, E., Van Gool, L.: Object tracking with an adaptive color-based particle filter. In: Van Gool, L. (ed.) DAGM 2002. LNCS, vol. 2449, pp. 353–360. Springer, Heidelberg (2002)
Rui, T., Zhang, Q., Zhou, Y., Xing, J.C.: Object tracking using particle filter in the wavelet subspace. Neurocomputing 119(7), 125–130 (2013)
Nouar, O.D., Ali, G., Raphael, C.: Improved object tracking with Camshift algorithm. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 657-660. IEEE, Piscataway (2006)
Kai, S., Shi-rong, L.: Combined algorithm with modified Camshift and Kalman Filter for multi-object tracking [J]. Information and Control 38(1), 11–16 (2009)
Yuan, G.-w., Gao, Y., Dan, X.: A Moving Objects Tracking Method Based on a Combination of Local Binary Pattern Texture and Hue. Procedia Engineering, Elsevier 15, 3964–3968 (2011)
Xin-jun, S., Jin-bo, C., Fa-wei., X.: Multiple Targets Tracking in Traffic Image Sequence. Journal of Changshu Institute Technology (Nature Sciences) 23(4), 82–86 (2009)
Xue, L., Fa-liang, C., Hua-jie, W.: An Object Tracking Method Based on Improved Camshift Algorithm. Microcomputer Information 23(21), 304–306 (2007)
Zhang, L.Y., Zhang, G.L.: Background motion model parameters estimation based on extended Kanade-Lucas tracker. Computer Applications 25(8), 1946–1947 (2005)
Yan, X.L., Liang, B., Zeng, G.H.: Object tracking method based on block motion estimation. Journal of Image and Graphics 13(10), 1869–1872 (2008)
Xin-jun, S., Jin-bo, C., Fa-wei, X.: Multiple Targets Tracking in Traffic Image Sequence. Journal of Changshu Institute Technology (Nature Sciences) 23(4), 82–86 (2009)
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Yuan, Gw., Zhang, Jx., Han, Yh., Zhou, H., Xu, D. (2015). A Multiple Objects Tracking Method Based on a Combination of Camshift and Object Trajectory Tracking. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9142. Springer, Cham. https://doi.org/10.1007/978-3-319-20469-7_18
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DOI: https://doi.org/10.1007/978-3-319-20469-7_18
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