Disagreement-Based multi-system tracking

Authors:
Quannan Li;Xinggang Wang;Wei Wang;Yuan Jiang;Zhi-Hua Zhou;Zhuowen Tu
Affiliations:
Lab of Neuro Imaging, University of California, Los Angeles;Huazhong University of Science and Technology, China;National Key Laboratory for Novel Software Technology, Nanjing University, China;National Key Laboratory for Novel Software Technology, Nanjing University, China;National Key Laboratory for Novel Software Technology, Nanjing University, China;Lab of Neuro Imaging, University of California, Los Angeles and Microsoft Research Asia, China
Venue:
ACCV'12 Proceedings of the 11th international conference on Computer Vision - Volume 2
Year:
2012

Citing 19
Cited 0

Combining labeled and unlabeled data with co-training

COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
A Theoretical Study on Six Classifier Fusion Strategies

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of Multiple Tracking Algorithms for Robust People Tracking

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Towards Robust Multi-cue Integration for Visual Tracking

ICVS '01 Proceedings of the Second International Workshop on Computer Vision Systems
Ensemble Methods in Machine Learning

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Exploiting unlabeled data in ensemble methods

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Robust Visual Tracking by Integrating Multiple Cues Based on Co-Inference Learning

International Journal of Computer Vision - Special Issue on Computer Vision Research at the Beckman Institute of Advanced Science and Technology
Democratic Co-Learning

ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Fusion of Intensity, Texture, and Color in Video Tracking Based on Mutual Information

AIPR '04 Proceedings of the 33rd Applied Imagery Pattern Recognition Workshop
Ensemble Tracking

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Tri-Training: Exploiting Unlabeled Data Using Three Classifiers

IEEE Transactions on Knowledge and Data Engineering
Democratic Integration: Self-Organized Integration of Adaptive Cues

Neural Computation
A General Framework for Combining Visual Trackers --- The "Black Boxes" Approach

International Journal of Computer Vision
Incremental Learning for Robust Visual Tracking

International Journal of Computer Vision
The value of agreement a new boosting algorithm

Journal of Computer and System Sciences
Analyzing Co-training Style Algorithms

ECML '07 Proceedings of the 18th European conference on Machine Learning
Semi-supervised On-Line Boosting for Robust Tracking

ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Semi-supervised learning by disagreement

Knowledge and Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we tackle the tracking problem from a fusion angle and propose a disagreement-based approach. While most existing fusion-based tracking algorithms work on different features or parts, our approach can be built on top of nearly any existing tracking systems by exploiting their disagreements. In contrast to assuming multi-view features or different training samples, we utilize existing well-developed tracking algorithms, which themselves demonstrate intrinsic variations due to their design differences. We present encouraging experimental results as well as theoretical justification of our approach. On a set of benchmark videos, large improvements (20% ~40%) over the state-of-the-art techniques have been observed.