Robust object tracking with background-weighted local kernels

Authors:
Jaideep Jeyakar;R. Venkatesh Babu;K. R. Ramakrishnan
Affiliations:
Adobe Systems, Bangalore, India;Yahoo! Labs, Bangalore, India;Department of Electrical Engineering, Indian Institute of Science, Bangalore, India
Venue:
Computer Vision and Image Understanding
Year:
2008

Citing 12
Cited 7

CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
On sequential Monte Carlo sampling methods for Bayesian filtering

Statistics and Computing
Kernel-Based Object Tracking

IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Kalman Filtering and Mean Shift for Real Time Eye Tracking under Active IR Illumination

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Mean-Shift Tracking via a New Similarity Measure

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
Coopetitive visual surveillance using model predictive control

Proceedings of the third ACM international workshop on Video surveillance & sensor networks
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
Robust tracking with motion estimation and local Kernel-based color modeling

Image and Vision Computing
Optical flow-based real-time object tracking using non-prior training active feature model

Real-Time Imaging

Adaptive pyramid mean shift for global real-time visual tracking

Image and Vision Computing
Adaptable Neural Networks for Objects' Tracking Re-initialization

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Object tracking using multiple fragments

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Dynamic tracking re-adjustment: a method for automatic tracking recovery in complex visual environments

Multimedia Tools and Applications
Tree-structured image difference for fast histogram and distance between histograms computation

Pattern Recognition Letters
Objects detection and tracking in highly congested traffic using compressed video sequences

ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Combining histogram-wise and pixel-wise matchings for kernel tracking through constrained optimization

Computer Vision and Image Understanding

Quantified Score

Hi-index	0.00

Visualization

Abstract

Object tracking is critical to visual surveillance, activity analysis and event/gesture recognition. The major issues to be addressed in visual tracking are illumination changes, occlusion, appearance and scale variations. In this paper, we propose a weighted fragment based approach that tackles partial occlusion. The weights are derived from the difference between the fragment and background colors. Further, a fast and yet stable model updation method is described. We also demonstrate how edge information can be merged into the mean shift framework without having to use a joint histogram. This is used for tracking objects of varying sizes. Ideas presented here are computationally simple enough to be executed in real-time and can be directly extended to a multiple object tracking system.