Take a look at the new version of this page: [ beta version ]. Tell us what you think.

ABSTRACT

One of the key problems in computer vision and pattern recognition is tracking. Multiple objects, occlusion, and tracking moving objects using a moving camera are some of the challenges that one may face in developing an effective approach for tracking. While there are numerous algorithms and approaches to the tracking problem with their own shortcomings, a less-studied approach considers swarm intelligence. Swarm intelligence algorithms are often suited for optimization problems, but require advancements for tracking objects in video. This paper presents an improved algorithm based on Bacterial Foraging Optimization in order to track multiple objects in real-time video exposed to full and partial occlusion, using video from both fixed and moving cameras. A comparison with various algorithms is provided.

REFERENCES

• 1: S. Birchfield, Elliptical Head Tracking Using Intensity Gradients and Color Histograms, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p.232, June 23-25, 1998
• 2: G. Bradski. The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000.
• 3: G.R. Bradski. Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal Q2 '98, 1998.
• 4: Anthony Jack Carlisle , Gerry V. Dozier, Applying the particle swarm optimizer to non-stationary environments, Auburn University, Auburn, AL, 2002
• 5: D. Comaniciu, V. Ramesh, and P. Meer. Real-time tracking of non-rigid objects using mean shift. CVPR 2000, 2:142--149 vol.2, 2000.
• 6: W. Ding, Z. Gong, S. Xie, and H. Zou. Real-time vision-based object tracking from a moving platform in the air. IEEE/RSJ Int. Conf. on Robots and Systems, pages 681--685, 2006.
• 7: David A. Forsyth , Jean Ponce, Computer Vision: A Modern Approach, Prentice Hall Professional Technical Reference, 2002
• 8: Michael Isard , Andrew Blake, C$ONDENSATION$—Conditional Density Propagation forVisual Tracking, International Journal of Computer Vision, v.29 n.1, p.5-28, Aug. 1998 [doi>10.1023/A:1008078328650]
• 9: Jinman Kang , Isaac Cohen , Chang Yuan, Detection and Tracking of Moving Objects from a Moving Platform in Presence of Strong Parallax, Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, p.10-17, October 17-20, 2005 [doi>10.1109/ICCV.2005.72]
• 10: J. Kennedy and R. Eberhart. Particle swarm optimization. Neural Networks, IEEE Int. Conf., 4:1942--1948 vol.4, 1995.
• 11: Y. Owechko and S. Medasani. Cognitive swarms for rapid detection of objects and associations in visual imagery. Swarm Intelligence Symposium, IEEE, pages 420--423, 2005.
• 12: K.M. Passino. Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine, Vol. 22, No. 3:52--67, 2002.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION
      I.4.8 Scene Analysis
          Subjects: Tracking

Additional Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Computer vision

General Terms:
Algorithms

Keywords:
bacterial foraging optimization, multi-object tracking, non-stationary video, occlusion, swarm intelligence