Tracking multiple objects in non-stationary video

Authors:
Hoang Nguyen;Bir Bhanu
Affiliations:
University of California, Riverside, Riverside, CA, USA;University of California, Riverside, Riverside, CA, USA
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 5
Cited 0

CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Computer Vision: A Modern Approach

Computer Vision: A Modern Approach
Elliptical Head Tracking Using Intensity Gradients and Color Histograms

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Applying the particle swarm optimizer to non-stationary environments

Applying the particle swarm optimizer to non-stationary environments
Detection and Tracking of Moving Objects from a Moving Platform in Presence of Strong Parallax

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01

Quantified Score

Hi-index	0.00

Visualization

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.