Robust Visual Object Tracking Using Multi-Mode Anisotropic Mean Shift and Particle Filters

This paper addresses issues in object tracking where videos contain complex scenarios. We propose a novel tracking scheme that jointly employs particle filters and multi-mode anisotropic mean shift. The tracker estimates the dynamic shape and appearance of objects, and also performs online learning...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems for video technology 2011-01, Vol.21 (1), p.74-87
Main Authors: Khan, Z H, Gu, I Y, Backhouse, A G
Format: Article
Language:English
Subjects:
Anisotropic mean shift
Anisotropy
Applied sciences
Detection, estimation, filtering, equalization, prediction
Distance learning
Dynamics
Estimates
Exact sciences and technology
Information, signal and communications theory
Joints
Kernel
Learning
Mathematical analysis
Mathematical models
multiple modes
multiple parts
object intersection
online learning
partial object occlusion
particle filters
Partitions
Pattern recognition
Prototypes
Robustness
Shape
Signal and communications theory
Signal processing
Signal, noise
Target tracking
Telecommunications and information theory
Tracking
Videos
Visual object tracking
Visualization
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Summary:This paper addresses issues in object tracking where videos contain complex scenarios. We propose a novel tracking scheme that jointly employs particle filters and multi-mode anisotropic mean shift. The tracker estimates the dynamic shape and appearance of objects, and also performs online learning of reference object. Several partition prototypes and fully tunable parameters are applied to the rectangular object bounding box for improving the estimates of shape and multiple appearance modes in the object. The main contributions of the proposed scheme include: 1) use a novel approach for online learning of reference object distributions; 2) use a five parameter set (2-D central location, width, height, and orientation) of rectangular bounding box as tunable variables in the joint tracking scheme; 3) derive the multi-mode anisotropic mean shift related to a partitioned rectangular bounding box and several partition prototypes; and 4) relate the bounding box parameter computation with the multi-mode mean shift estimates by combining eigen decomposition, geometry of subareas, and weighted average. This has led to more accurate and efficient tracking where only small number of particles (
ISSN:1051-8215
1558-2205
DOI:10.1109/TCSVT.2011.2106253