Online visual tracking with histograms and articulating blocks

Authors:
S.M. Shahed Nejhum;Jeffrey Ho;Ming-Hsuan Yang
Affiliations:
Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, United States;Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, United States;Electrical Engineering and Computer Science, University of California, Merced, CA 95344, United States
Venue:
Computer Vision and Image Understanding
Year:
2010

Citing 24
Cited 5

Geodesic Active Contours

International Journal of Computer Vision
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation

International Journal of Computer Vision
The visual analysis of human movement: a survey

Computer Vision and Image Understanding
Human motion analysis: a review

Computer Vision and Image Understanding
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3D Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture

Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Active Appearance Models

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Elliptical Head Tracking Using Intensity Gradients and Color Histograms

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Model-based tracking of self-occluding articulated objects

ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Active Blobs

ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
On-Line Selection of Discriminative Tracking Features

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking Objects Using Density Matching and Shape Priors

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pictorial Structures for Object Recognition

International Journal of Computer Vision
Interactive Graph Cut Based Segmentation with Shape Priors

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Spatiograms versus Histograms for Region-Based Tracking

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Efficient Optimal Kernel Placement for Reliable Visual Tracking

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
An Adaptive Appearance Model Approach for Model-based Articulated Object Tracking

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Fragments-based Tracking using the Integral Histogram

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Training Deformable Models for Localization

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Tunable Kernels for Tracking

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Object tracking: A survey

ACM Computing Surveys (CSUR)
A survey of advances in vision-based human motion capture and analysis

Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour

Robust Visual Tracking Using an Effective Appearance Model Based on Sparse Coding

ACM Transactions on Intelligent Systems and Technology (TIST)
Visual tracking via multiple representative basic appearance models based on l 1 minimization

Proceedings of the 2012 ACM Research in Applied Computation Symposium
Structured visual tracking with dynamic graph

ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
A survey of appearance models in visual object tracking

ACM Transactions on Intelligent Systems and Technology (TIST) - Survey papers, special sections on the semantic adaptive social web, intelligent systems for health informatics, regular papers
Weighted attentional blocks for probabilistic object tracking

The Visual Computer: International Journal of Computer Graphics

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose an algorithm for accurate tracking of articulated objects using online update of appearance and shape. The challenge here is to model foreground appearance with histograms in a way that is both efficient and accurate. In this algorithm, the constantly changing foreground shape is modeled as a small number of rectangular blocks, whose positions within the tracking window are adaptively determined. Under the general assumption of stationary foreground appearance, we show that robust object tracking is possible by adaptively adjusting the locations of these blocks. Implemented in MATLAB without substantial optimization, our tracker runs already at 3.7 frames per second on a 3GHz machine. Experimental results have demonstrated that the algorithm is able to efficiently track articulated objects undergoing large variation in appearance and shape.