Abstract
A new method for real-time occlusion-robust tracking is proposed. By analyzing the process of occlusion occurrence, we present a fast and effective occlusion detection algorithm based on the spatio-temporal context information. As a result, we can always obtain correct target location using adaptive template matching with patch-based structure description, regardless of the occlusion situation. Our extensive experiments on many sequences verify the good performance of our algorithm. In addition, based on the framework of our algorithm and properties we find, more effective occlusion-robust tracking algorithms can be developed.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys (CSUR) 38(4), 13 (2006)
Adam, A., Rivlin, E., Shimshoni, I.: Robust fragments-based tracking using the integral histogram. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE (2006)
Kwon, J., Lee, K.M.: Tracking of a non-rigid object via patch-based dynamic appearance modeling and adaptive basin hopping monte carlo sampling. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009. IEEE (2009)
Dihl, L., Jung, C.R., Bins, J.: Robust adaptive patch-based object tracking using weighted vector median filters. In: 2011 24th SIBGRAPI Conference on Graphics, Patterns and Images (Sibgrapi). IEEE (2011)
Kalal, Z., Mikolajczyk, K., Matas, J.: Tracking-learning-detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 34(7), 1409–1422 (2012)
Zhang, K., et al.: Fast Tracking via Spatio-Temporal Context Learning. arXiv preprint arXiv:1311.1939 (2013)
Deng, Y., et al.: A symmetric patch-based correspondence model for occlusion handling. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2. IEEE (2005)
Rosten, E., Drummond, T.W.: Machine learning for high-speed corner detection. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 430–443. Springer, Heidelberg (2006)
Lucas, B.D., Kanade, T.: An iterative image registration technique with an application to stereo vision. IJCAI 81 (1981)
Donoser, M., Bischof, H.: Efficient maximally stable extremal region (MSER) tracking. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE (2006)
Bouguet, J.-Y.: Pyramidal implementation of the affine lucas kanade feature tracker description of the algorithm. Intel Corporation 2, 3 (2001)
Wu, Y., Lim, J., Yang, M.-H.: Online object tracking: A benchmark. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Tian, J., Zhou, Y. (2014). Real-Time Patch-Based Tracking with Occlusion Handling. In: Loo, C.K., Yap, K.S., Wong, K.W., Beng Jin, A.T., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8836. Springer, Cham. https://doi.org/10.1007/978-3-319-12643-2_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-12643-2_26
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12642-5
Online ISBN: 978-3-319-12643-2
eBook Packages: Computer ScienceComputer Science (R0)