Jungdong Jin
ABSTRACT
In this paper, we present the design and implementation of real-time multiple object centroid tracking for gesture recognition. Our multiple object tracking design consists of four stages: preprocessing, local intensity accumulation, object observation, and particle filter. We implemented the proposed hardware architecture using Verilog Hardware Description Language (HDL) on a Xilinx Virtex-5 LX330 field programmable gate array (FPGA). We focus on two main performances: the trajectory accuracy of moving objects and real-time processing. The performance of the proposed system was evaluated through several experiments. In addition, our processing speed was compared with the same algorithm based on software. Based on the results, we can guarantee that our multiple object tracking design is suitable for gesture recognition in cluttered environments.
- Ristic B., Arulampalam S., Gordon N., "Beyond the Kalman Filter PARTICLE FILTERS FOR TRACKING APPLICATIONS", Artech House Publishers, February, 2004.Google Scholar
- Yilmaz A., Javed O., Shah M., "Object Tracking: A Survey", ACM Computing Surveys, Vol. 38, Article No. 13, December, 2006. Google ScholarDigital Library
- Nummiaro K., Koller-Meier E., Van Gool L., "A Color-based Particle Filter", in Proc. Workshop on Generative-Model-Based Vision, pp. 53--60, June, 2002.Google Scholar
- Nguyen T. T., Jeon J. W., "Camera Auto--Exposing and Auto--Focusing for Edge--Related Applications using a Particle Filter", IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1177--1182, October, 2010.Google Scholar
- Hue C., Cadre J-P. L., Perez P., "Tracking multiple objects with particle filtering", IEEE Transactions on Aerospace and Electronic Systems, Vol. 38, pp. 791--812, July, 2002.Google ScholarCross Ref
- Hu W., Li X, Luo W., Zhang X., "Single and Multiple Object Tracking Using Log-Euclidean Riemannian Subspace and Block-Division Appearance Model", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PP, pp. 1, 2012. Google ScholarDigital Library
- Chen B-J., Huang C-M., Fu L-C., "Real-Time Visual Tracking with Adaptive Particle Filter for Human-Machine Interaction", SICE Annual Conference, pp. 1344--1349, September, 2011.Google Scholar
- Zhang X., Hu W., Qu W., Maybank S., "Multiple Object Tracking Via Species-Based Particle Swarm Optimization", IEEE Transactions on Circuits and Systems for Video Technology, Vol. 20, pp. 1590--1602, November, 2010. Google ScholarDigital Library
- Morelande R. M., Kreucher M. C., Kastella K., "A Bayesian Approach to Multiple Target Detection and Tracking", IEEE Transactions on Signal Processing, Vol. 55, No. 5, pp. 1589--1604, May, 2007. Google ScholarDigital Library
- Lenz C., Panin G., Roder T., Wojtczyk M., Knoll A., "Hardware-assisted multiple object tracking for human-robot-interaction", ACM/IEEE International Conference on Human-Robot Interaction, pp. 283--284, March, 2009. Google ScholarDigital Library
- Miao L, Zhang J. J., Chakrabarti C., Papandreou-Suppappola A., Kovvali N., "Real-time closed-loop tracking of an unknown number of neural sources using probability hypothesis density particle filtering", IEEE Workshop on Signal Processing Systems, pp. 367--372, October, 2011.Google Scholar
- Cho J. U., Jin S. H., Pham X. D., Jeon J. W., "Object Tracking Circuit using Particle Filter with Multiple Features", International Joint Conference SICE-ICASE, pp. 1431--1436, October, 2006.Google Scholar
- Cho J. U., Jin S. H., Pham X. D., Jeon J. W., "Multiple Objects Tracking Circuit using Particle Filters with Multiple Features", IEEE International Conference on Robotics and Automation, pp. 4639--4644, April, 2007.Google Scholar
- A Doucet, J F G de Freitas & N J Gordon, An introduction to sequential Monte Carlo methods, in sequential Monte Carlo Methods in Practice, eds A Doucet, J F G de Freitas and N J Gordon, 2001, Springer-Verlag: New YorkGoogle Scholar
- A Doucet, On Sequential Monte Carlo Methods for Bayesian Filtering. 1998, Technical Report, University of Cambridge, UK, Department of Engineering.Google Scholar
- N Gordon, D Salmond & A F M Smith, Novel Approach to Nonlinear and Nonlinear and Non-Gaussian Bayesian State Estimation. IEE Proceedings-F, 1993, Volume 140, p. 107--113.Google Scholar
- Xilinx XST User Guide, http://www.xilinx.comGoogle Scholar
Index Terms
- Real-time multiple object centroid tracking for gesture recognition based on FPGA
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