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Adaptation of the Mean Shift Tracking Algorithm to Monochrome Vision Systems for Pedestrian Tracking Based on HoG-Features
Technical Paper
2014-01-0170
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
The mean shift tracking algorithm has become a standard in the field of visual object tracking, caused by its real time capability and robustness to object changes in pose, size, or illumination. The standard mean shift tracking approach is an iterative procedure that is based on kernel weighted color histograms for object modelling and the Bhattacharyyan coefficient as a similarity measure between target and candidate histogram model. The benefits of the approach could not been transferred to monochrome vision systems yet, because the loss of information from color to grey-scale histogram object models is too high and the system performance drops seriously. We propose a new framework that solves this problem by using histograms of HoG-features as object model and the SOAMST approach by Ning et al. for track estimation. Mean shift tracking requires a histogram for object modelling. In the proposed framework a set of high dimensional HoG-features is clustered via K-means and features inside the object area are matched to the cluster-centers via a nearest neighbor search. This procedure is comparable to a Bag of Words algorithm. The proposed system is evaluated for advanced driver assistance systems and it is shown that the framework can be used as a reliable visual tracking system for a pedestrian recognition module.
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Schugk, D., Kummert, A., and Nunn, C., "Adaptation of the Mean Shift Tracking Algorithm to Monochrome Vision Systems for Pedestrian Tracking Based on HoG-Features," SAE Technical Paper 2014-01-0170, 2014, https://doi.org/10.4271/2014-01-0170.Also In
References
- Yilmaz A. , Javed O. and Shah M. Object tracking: A survey ACM Comput. Surv. 38 4 13 December 2006 10.1145/1177352.1177355
- Lucas B. D. and Kanade T. An iterative image registration technique with an application to stereo vision Proceedings of the 7th international joint conference on Artificial intelligence Volume 2 San Francisco, CA, USA Morgan Kaufmann Publishers Inc. 1981 674 679
- Comaniciu D. , Ramesh V. and Meer P. Kernel-based object tracking Pattern Analysis and Machine Intelligence, IEEE Transactions on 25 5 564 577 2003 10.1109/TPAMI.2003.1195991
- Zitová B. and Flusser J. Image registration methods: a survey Image Vision Comput. 21 11 977 1000 2003 10.1016/S0262-8856(03)00137-9
- Fukunaga K. and Hostetler L. The estimation of the gradient of a density function, with applications in pattern recognition Information Theory, IEEE Transactions on 21 1 32 40 1975 10.1109/TIT.1975.1055330
- Comaniciu D. and Meer P. Mean shift analysis and applications Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on Kerkyra 1999 10.1109/ICCV.1999.790416
- Comaniciu D. , Ramesh V. and Meer P. Real-time tracking of non-rigid objects using mean shift Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on Hilton Head Island, SC 2000 10.1109/CVPR.2000.854761
- Collins R. Mean-shift blob tracking through scale space Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on Madison, Wisconsin 2003 10.1109/CVPR.2003.1211475
- Yan Y. , Huang X. , Xu W. and Shen L. Robust Kernel-Based Tracking with Multiple Subtemplates in Vision Guidance System Sensors 12 2 1990 2004 2012 10.3390/s120201990
- Bradski G. R. Computer vision face tracking for use in a perceptual user interface Intel Technology Journal 2 1998
- Lee L.-K. , An S.-Y. and Oh S.-y. Robust visual object tracking with extended CAMShift in complex environments IECON 2011 - 37th Annual Conference on IEEE Industrial Electronics Society Melbourne, VIC 2011 10.1109/IECON.2011.6120057
- Zivkovic Z. and Krose B. An EM-like algorithm for color-histogram-based object tracking Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on Washington, DC 2004 10.1109/CVPR.2004.1315113
- Ning J. , Zhang L. , Zhang D. and Wu C. Scale and orientation adaptive mean shift tracking Computer Vision, IET 6 1 52 61 2012 10.1049/ietcvi.2010.0112
- Ning J. , Zhang L. , Zhang D. and Wu C. Robust mean-shift tracking with corrected background-weighted histogram Computer Vision, IET 6 1 62 69 2012 10.1049/iet-cvi.2009.0075
- Dalal N. and Triggs B. Histograms of oriented gradients for human detection Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on San Diego, CA, USA 2005 10.1109/CVPR.2005.177
- Enzweiler M. and Gavrila D. Monocular Pedestrian Detection: Survey and Experiments Pattern Analysis and Machine Intelligence, IEEE Transactions on 31 12 2179 2195 2009 10.1109/TPAMI.2008.260
- Zheng G. and Chen Y. A review on vision-based pedestrian detection Global High Tech Congress on Electronics (GHTCE), 2012 IEEE Shenzhen 2012 10.1109/GHTCE.2012.6490122
- Viola P. a. J. M. Rapid object detection using a boosted cascade of simple features Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on Kauai, HI 2001 10.1109/CVPR.2001.990517