Multiple Object Tracking using Particle Swarm Optimization
This paper presents a particle swarm optimization
(PSO) based approach for multiple object tracking based on histogram
matching. To start with, gray-level histograms are calculated to
establish a feature model for each of the target object. The difference
between the gray-level histogram corresponding to each particle in the
search space and the target object is used as the fitness value. Multiple
swarms are created depending on the number of the target objects
under tracking. Because of the efficiency and simplicity of the PSO
algorithm for global optimization, target objects can be tracked as
iterations continue. Experimental results confirm that the proposed
PSO algorithm can rapidly converge, allowing real-time tracking of
each target object. When the objects being tracked move outside the
tracking range, global search capability of the PSO resumes to re-trace
the target objects.
[1] J. Kennedy and R.C. Eberhart, "Particle Swarm Optimization," Proc.
IEEE Int. Conf. Neural Network, Piscataway, 1995, pp. 1942 - 1948.
[2] K. Nummiaro, E. Koller-Meier, and L.V. Gool, "An Adaptive
Color-Based Particle Filter," Image and Vision Computing, Vol. 21, pp.
99-110, 2003.
[3] C. Yang, R. Duraiswami, and L. Davias, "Fast multiple object tracking via
hierarchical particle filter," Conference on Computer Vision, 2005, vol. 1,
pp.212-219.
[4] L. Kong and P. Wu, "Accurate particle filter tracking with SIFT keypoint,"
Journal of Harbin Institute of Technology (NEW Series), Vol. 15, pp.
209-213, 2008.
[5] T. Kobayashi, K. Nakagawa, J. Imae, and G. Zhai, "Real Time Object
Tracking on Video Image Sequence Using Particle Swarm Optimization,"
International Conference on Control, Automation and Systems, 2007, pp.
1773-1778.
[6] Y. Jin and F. Mokhtarian, "Variational Particle Filter for Multi-Object
Tracking," IEEE 11th International Conference on Computer Vision,
2007, pp.1-8.
[7] Y. Fang, H. Wang, S. Mao, and X. Wu, "Multi-object Tracking Based on
Region Corresponding and Improved Color-Histogram Matching," IEEE
International Symposium on Signal Processing and Information
Technology, 2007, pp.1-4.
[8] S.S. Pathan and A.B. Michaelis, "Intelligent feature-guided multi-object
tracking using Kalman filter," International Conference on Computer,
Control and Communication, 2009, pp.1-6.
[9] http://140.113.87.112/vol_2/skill_6.htm.
[10] S. McKenna, S. Jabri, Z. Duric, and A. Rosenfeld, and H. Wechsler,
"Tracking groups of people," Comput. Vis. Image Understanding, Vol.
80, pp. 42-56, 2000.
[11] C.R.Wren, A. Azarbayejani, T. Darrell, and A.P. Pentland, "Pfinder:
real-time tracking of the human body," IEEE Trans. Pattern Anal.
Machine Intell., Vol. 19, pp. 780-785, 1997.
[12] Paragios, N., Deriche, R.: Geodesic active contours and level sets for the
detection and tracking of moving objects. IEEE Trans. Pattern Anal.
Machine Intell., 22, 266-280 (2000)
[13] A. Blak and M. Isard, "3D Position, Altitude and Shape Input Using
Video Tracking of Hands and Lips," Proc. Computer Graphics,
SIGGRAPH, 1994, pp. 71-78.
[14] D. Terzopoulos and R. Szeliski, "Tracking with Kalman Snakes," Active
Vision, A. Blake and A. Yuille, eds., pp. 3-20, MIT Press, 1992.
[15] D.S. Jang and H.I Choi, "Active models for tracking moving objects,"
Pattern Recognit., Vol. 33, pp. 1135-1146, 2000.
[16] J. Malik and S. Russell, Traffic surveillance and detection technology
development (new traffic sensor technology), Univ. of California,
Berkeley, 1996.
[17] I.A. Karaulova, P. M. Hall, and A.D. Marshall, "A hierarchical model of
dynamics for tracking people with a single video camera," Proc. British
Machine Vision Conf., 2000, pp. 262-352.
[18] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking,"
Real-Time Vision & Modeling, Dept., Siemens Corporate Res., Princeton,
NJ, USA.
[19] C.H. Chen and M.C. Yan, "PSO-Based Multiple People Tracking,"
Communications in Computer and Information Science, Vol. 166, pp.
267-276, 2011.
[20] M. Clerc and J. Kennedy, "The particle swarm explosion, stability, and
convergence in a multidimensional complex space," IEEE Transaction on
Evolutionary Computation, Vol. 6, pp. 58-73, 2002.
[21] Z.L. Gaing, "A Particle Swarm Optimization Approach for Optimum
Design of PID Controller in AVR System," IEEE Transactions on Energy
Conversion, Vol. 19, pp. 384-391, 2004.
[22] J. Kennedy and R.C Eberhart, "A discrete binary version of the particle
swarm algorithm," Proceedings IEEE Int-l. Conf. on Systems, Man, and
Cybernetics, 1997, vol. 5, pp. 4104-4108.
[23] Y Zheng and Y. Meng, "Swarm intelligence based dynamic object
tracking," IEEE Congress on Evolutionary Computation, 2008, pp.
405-412.
[1] J. Kennedy and R.C. Eberhart, "Particle Swarm Optimization," Proc.
IEEE Int. Conf. Neural Network, Piscataway, 1995, pp. 1942 - 1948.
[2] K. Nummiaro, E. Koller-Meier, and L.V. Gool, "An Adaptive
Color-Based Particle Filter," Image and Vision Computing, Vol. 21, pp.
99-110, 2003.
[3] C. Yang, R. Duraiswami, and L. Davias, "Fast multiple object tracking via
hierarchical particle filter," Conference on Computer Vision, 2005, vol. 1,
pp.212-219.
[4] L. Kong and P. Wu, "Accurate particle filter tracking with SIFT keypoint,"
Journal of Harbin Institute of Technology (NEW Series), Vol. 15, pp.
209-213, 2008.
[5] T. Kobayashi, K. Nakagawa, J. Imae, and G. Zhai, "Real Time Object
Tracking on Video Image Sequence Using Particle Swarm Optimization,"
International Conference on Control, Automation and Systems, 2007, pp.
1773-1778.
[6] Y. Jin and F. Mokhtarian, "Variational Particle Filter for Multi-Object
Tracking," IEEE 11th International Conference on Computer Vision,
2007, pp.1-8.
[7] Y. Fang, H. Wang, S. Mao, and X. Wu, "Multi-object Tracking Based on
Region Corresponding and Improved Color-Histogram Matching," IEEE
International Symposium on Signal Processing and Information
Technology, 2007, pp.1-4.
[8] S.S. Pathan and A.B. Michaelis, "Intelligent feature-guided multi-object
tracking using Kalman filter," International Conference on Computer,
Control and Communication, 2009, pp.1-6.
[9] http://140.113.87.112/vol_2/skill_6.htm.
[10] S. McKenna, S. Jabri, Z. Duric, and A. Rosenfeld, and H. Wechsler,
"Tracking groups of people," Comput. Vis. Image Understanding, Vol.
80, pp. 42-56, 2000.
[11] C.R.Wren, A. Azarbayejani, T. Darrell, and A.P. Pentland, "Pfinder:
real-time tracking of the human body," IEEE Trans. Pattern Anal.
Machine Intell., Vol. 19, pp. 780-785, 1997.
[12] Paragios, N., Deriche, R.: Geodesic active contours and level sets for the
detection and tracking of moving objects. IEEE Trans. Pattern Anal.
Machine Intell., 22, 266-280 (2000)
[13] A. Blak and M. Isard, "3D Position, Altitude and Shape Input Using
Video Tracking of Hands and Lips," Proc. Computer Graphics,
SIGGRAPH, 1994, pp. 71-78.
[14] D. Terzopoulos and R. Szeliski, "Tracking with Kalman Snakes," Active
Vision, A. Blake and A. Yuille, eds., pp. 3-20, MIT Press, 1992.
[15] D.S. Jang and H.I Choi, "Active models for tracking moving objects,"
Pattern Recognit., Vol. 33, pp. 1135-1146, 2000.
[16] J. Malik and S. Russell, Traffic surveillance and detection technology
development (new traffic sensor technology), Univ. of California,
Berkeley, 1996.
[17] I.A. Karaulova, P. M. Hall, and A.D. Marshall, "A hierarchical model of
dynamics for tracking people with a single video camera," Proc. British
Machine Vision Conf., 2000, pp. 262-352.
[18] D. Comaniciu, V. Ramesh, and P. Meer, "Kernel-based object tracking,"
Real-Time Vision & Modeling, Dept., Siemens Corporate Res., Princeton,
NJ, USA.
[19] C.H. Chen and M.C. Yan, "PSO-Based Multiple People Tracking,"
Communications in Computer and Information Science, Vol. 166, pp.
267-276, 2011.
[20] M. Clerc and J. Kennedy, "The particle swarm explosion, stability, and
convergence in a multidimensional complex space," IEEE Transaction on
Evolutionary Computation, Vol. 6, pp. 58-73, 2002.
[21] Z.L. Gaing, "A Particle Swarm Optimization Approach for Optimum
Design of PID Controller in AVR System," IEEE Transactions on Energy
Conversion, Vol. 19, pp. 384-391, 2004.
[22] J. Kennedy and R.C Eberhart, "A discrete binary version of the particle
swarm algorithm," Proceedings IEEE Int-l. Conf. on Systems, Man, and
Cybernetics, 1997, vol. 5, pp. 4104-4108.
[23] Y Zheng and Y. Meng, "Swarm intelligence based dynamic object
tracking," IEEE Congress on Evolutionary Computation, 2008, pp.
405-412.
@article{"International Journal of Electrical, Electronic and Communication Sciences:59640", author = "Chen-Chien Hsu and Guo-Tang Dai", title = "Multiple Object Tracking using Particle Swarm Optimization", abstract = "This paper presents a particle swarm optimization
(PSO) based approach for multiple object tracking based on histogram
matching. To start with, gray-level histograms are calculated to
establish a feature model for each of the target object. The difference
between the gray-level histogram corresponding to each particle in the
search space and the target object is used as the fitness value. Multiple
swarms are created depending on the number of the target objects
under tracking. Because of the efficiency and simplicity of the PSO
algorithm for global optimization, target objects can be tracked as
iterations continue. Experimental results confirm that the proposed
PSO algorithm can rapidly converge, allowing real-time tracking of
each target object. When the objects being tracked move outside the
tracking range, global search capability of the PSO resumes to re-trace
the target objects.", keywords = "multiple object tracking, particle swarm
optimization, gray-level histogram, image", volume = "6", number = "8", pages = "846-4", }
