Efficient Mean Shift Clustering Using Exponential Integral Kernels
This paper presents a highly efficient algorithm for detecting and tracking humans and objects in video surveillance sequences. Mean shift clustering is applied on backgrounddifferenced image sequences. For efficiency, all calculations are performed on integral images. Novel corresponding exponential integral kernels are introduced to allow the application of nonuniform kernels for clustering, which dramatically increases robustness without giving up the efficiency of the integral data structures. Experimental results demonstrating the power of this approach are presented.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1073241Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1218
 K. Fukunaga and L. Hostetler, "The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition," IEEE Transactions on Information Theory, vol. 21, pp. 32-40, 1975.
 C. Beleznai, B. Fr├╝hst├╝ck, H. Bischof, and W. Kropatsch, "Detecting Humans in Groups Using a Fast Mean Shift Procedure", In Proceedings of the 28th Workshop of the Austrian Association for Pattern Recognition, vol. 179, pp. 71-78, 2004.
 P. Viola, M. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features", IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'01) - Volume 1, pp. 511, 2001
 Y. Cheng, "Mean Shift, Mode Seeking and Clustering," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 17, pp. 790-799, 1995.
 F. Matusek, S. Sutor, K.Kruse, K. Kraus and R. Reda, Large-Scale Video Surveillance Systems: New Performance Parameters and Metrics, to be published at in Press, The Third International Conference on Internet Monitoring and Protection, Bucharest June 29 - July 5, 2008
 A. Yilmaz, K. Shafique, and M. Shah, "Target Tracking in Airborne Foward Looking Infrared Imagery," Image and Vision Computing Journal, vol. 21, pp. 623-635, 2003.
 D. Comaniciu, V. Ramesch, and P. Meer, "Kernel-Based Object Tracking," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, pp. 564-577, 2003.
 S. Sutor, "A Mean Shift Based Approach towards Automated Person Tracking", Master Thesis, Vienna University of Technology, 2007