Abstract
The large number of high-powered computers, the availability of high quality and inexpensive video cameras, and the increasing need for automated video analysis has generated a great deal of interest in object tracking algorithms. Real time object tracking has many practical applications, both commercial and military, such as visual surveillance, traffic monitoring, vehicle navigation, precision targeting, perceptual user interfaces and artificial intelligence.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-Based Object Tracking”, IEEE Trans Pattern Analysis and Machine Intelligence, Vol. 25, No. 5, 2003 pp 564–575
D. Comaniciu, V. Ramesh, and P. Meer, “Real-Time Tracking of Non-Rigid Objects Using Mean Shift,” Proc. of IEEE Conf.on Comp. Vision and Pattern Recog., 2000, pp 142–149
N. Peng, Yang J, Liu Z, et al.,” Automatic Selection of Kernel-Bandwidth for Mean-Shift Object Tracking”, Journal of Software, Vol. 16, No. 9, 2005, pp.1542–1550
D. Comaniciu, “An Algorithm for Data-Driven Bandwidth Selection”, in IEEE Trans.On pattern analysis and machine intelligence. Vol. .25, No.2, 2003, pp.281–288
N. Peng, Yang J, Liu Z., “Mean Shift BlobTracking With Kernel Histogram Filtering and Hypothesis Testing”, Pattern Recognition Letters, Vol. 26, No.5, 2005, pp. 605–614
R T. Collins, Y. Liu, and M. Leordeanu, “Online Selection Of Discriminative Tracking Features”, IEEE Trans. on pattern analysis and machine intelligence, Vol. 27, No.10, 2005, pp 1631–1643
Z. Wen, Z. Cai, “A Robust Object TrackingApproach using Mean Shift”, Third International Conference on Natural Computation (ICNC 2007), Sep 2007
M. Shah “Object Tracking: A Survey” ACM Computing Survey, Vol. 38, No 4, Article 13, Dec 2006
Xu Dong, Y. Wang, and Jinwen “Applying a New Spatial Color Histogram in Mean- Shift Based Tracking Algorithm”, Image and Vision Comp., Univ. of Otago, New Zealand, 2005
R. Mehmood, M. Ali, I. Taj “Applying Centroid Based Adjustment to Kerne Based Object Tracking for ImprovingL ocalization”, IEEE, 2009
K. Fukanaga and L. Hostetler, “The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition,” IEEE Trans. Info. Theory Vol.21, 1975, pp 32–40
C. Yunqiang and R. Yong, “Real Time Object Tracking in Video Sequences”, Signals and Communications Technologies, Interactive Video, Part II: 2006, pp. 67–88
A. Lehuger, P. Lechat and P. Perez, “An Adaptive Mixture Color Model for Robust Visual Tracking”, in Proc. IEEE Int. Conf. on Image Process, Oct 2006, pp. 573–576
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer India Pvt. Ltd
About this paper
Cite this paper
Wakode, S., Krithiga, A., Warhade, K.K., Wadhai, V.M. (2011). Kernel based object tracking with enhanced localization. In: Pise, S.J. (eds) Thinkquest~2010. Springer, New Delhi. https://doi.org/10.1007/978-81-8489-989-4_5
Download citation
DOI: https://doi.org/10.1007/978-81-8489-989-4_5
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-8489-988-7
Online ISBN: 978-81-8489-989-4
eBook Packages: Computer ScienceComputer Science (R0)