Abstract
In this paper, a new method is presented for long-term object tracking in surveillance videos. The developed method combines surrounding image sampling, saliency map, self-organizing map neural network, k-Means segmentation and similarity measurement. Saliency map can provide valuable information to reduce over-segmentation. The surrounding image sampling always extracts the regions which are close to the centroid of the latest tracked target. The self-organizing map quantizes the image samples into a topological space, it compresses information while preserving the most important topological and metric relationships of the primary features. The k-Means algorithm will generate segmentation based on the output of the self-organizing map. Then, according to the segmentation results of the new frame and the first frame, a similarity measurement is used to get the most similar image sample to the specified object in the first frame and thus object position in new frame is found. We apply the developed method to track objects in the real-world environment of surveillance videos, computer simulations indicate that the proposed approach presents better results than those obtained by a direct method approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dundar, A., Jin, J., Culurciello, E.: Visual tracking with similarity matching ratio, arXiv preprint arXiv:1209.2696 (2012)
Zhang, T., Ghanem, B., Liu, S., Ahuja, N.: Robust visual tracking via multi-task sparse learning. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 16-21, pp. 2042–2049 (2012)
Jin, J., Dundar, A., Bates, J., Farabet, C., Culurciello, E.: Tracking with deep neural networks. In: 2013 47th Annual Conference on Information Sciences and Systems (CISS), March 20-22, pp. 1–5 (2013)
Angelova, D., Mihaylova, L., Petrov, N., Gning, A.: Extended object tracking with convolution particle filtering. In: 2012 6th IEEE International Conference on Intelligent Systems (IS), September 6-8, pp. 96–101 (2012)
Avidan, S.: Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(2), 261–271 (2007)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Kohonen, T.: Self-Organize maps, Berlin, 3rd edn. Springer, Germany (2000)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)
Borsotti, M., Campadelli, P., Schettini, R.: Quantitative evaluation of color image segmentation results. Pattern Recognition Letters 19(8), 741–747 (1998)
Hunt, R.W.G., Pointer, M.R.: Measuring color, 4th edn. Wiley & Sons, Chichester (2011)
Ko, B.C., Nam, J.-Y.: Object-of-interest image segmentation based on human attention and semantic region clustering. Journal of Optical Society of America A 23(10), 2462–2470 (2006)
Grabner, H., Bischof, H.: On-Line Boosting and Vision. In: Proc. IEEE CS Conf. Computer Vision and Pattern Recognition (2006)
Babenko, B., Yang, M.-H., Belongie, S.: Visual Tracking with Online Multiple Instance Learning. In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (2009)
Yu, Q., Dinh, T.B., Medioni, G.: Online Tracking and Reacquisition Using Co-Trained Generative and Discriminative Trackers. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 678–691. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, Y., Tang, Y., Fang, B., Shang, Z., Suen, C.Y. (2014). Self-organizing Map-Based Object Tracking with Saliency Map and K-Means Segmentation. In: Li, S., Liu, C., Wang, Y. (eds) Pattern Recognition. CCPR 2014. Communications in Computer and Information Science, vol 483. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45646-0_45
Download citation
DOI: https://doi.org/10.1007/978-3-662-45646-0_45
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45645-3
Online ISBN: 978-3-662-45646-0
eBook Packages: Computer ScienceComputer Science (R0)