SWARMTRACK: A PARTICLE SWARM APPROACH TO VISUAL TRACKING

A new approach to solve the object tracking problem is proposed using a Swarm Intelligence metaphor. It is based on a prey-predator scheme with a swarm of predator particles defined to track a herd of prey pixels using the intensity of its flavours. The method is described, including the definition of predator particles’ behaviour as a set of rules in a Boids fashion. Object tracking behaviour emerges from the interaction of individual particles. The paper includes experimental evaluations with video streams that illustrate the robustness and efficiency for real-time vision based tasks using a general purpose computer.

[1]  H. Redkey,et al.  A new approach. , 1967, Rehabilitation record.

[2]  Mario Hernández-Tejera,et al.  An Heuristic Search based Approach for Moving Objects Tracking , 2005, IJCAI.

[3]  Andrew Blake,et al.  A framework for spatiotemporal control in the tracking of visual contours , 1993, International Journal of Computer Vision.

[4]  Craig W. Reynolds.  Flocks, herds, and schools: a distributed behavioral model , 1987, SIGGRAPH.

[5]  Cayetano Guerra,et al.  A New Approach to the Template Update Problem , 2005, IbPRIA.

[6]  Takahiro Ishikawa,et al.  The template update problem , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[8]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[9]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[11]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[13]  Alan L. Yuille,et al.  Feature extraction from faces using deformable templates , 2004, International Journal of Computer Vision.

[14]  Rafael Murrieta-Cid,et al.  3-D Modelling and Robot Localization from Visual and Range Data in Natural Scenes , 1999, ICVS.

[15]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[16]  Matthew Turk,et al.  Computer vision in the interface , 2004, CACM.

[17]  Y. Aloimonos.  Active Perception , 1993 .

[18]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[19]  William Rucklidge,et al.  Efficient Visual Recognition Using the Hausdorff Distance , 1996, Lecture Notes in Computer Science.

[20]  Hwann-Tzong Chen,et al.  Real-time tracking using trust-region methods , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[23]  Alex Pentland,et al.  Perceptual Intelligence , 1999, HUC.

[24]  M. Hernandez-Tejera,et al.  A fast and accurate tracking approach for automated visual surveillance , 2005, Proceedings 39th Annual 2005 International Carnahan Conference on Security Technology.

[25]  Michael Isard,et al.  CONDENSATION—Conditional Density Propagation for Visual Tracking , 1998, International Journal of Computer Vision.