CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Sparse Features in Granular Space for Multi-View Face Detection
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A particle filter for joint detection and tracking of color objects
Image and Vision Computing
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Robust Real-Time Visual Tracking Using Pixel-Wise Posteriors
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Adaptive pyramid mean shift for global real-time visual tracking
Image and Vision Computing
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Active contours for tracking distributions
IEEE Transactions on Image Processing
Distribution fields for tracking
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Real-time visual tracking via online weighted multiple instance learning
Pattern Recognition
Real-time compressive tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Hi-index | 0.00 |
This paper presents an improved multiple instance learning (MIL) tracker representing target with Distribution Fields (DFs) and building a weighted-geometric-mean MIL classifier. Firstly, we adopt DF layer as feature instead of traditional Haar-like one to model the target thanks to the DF specificity and the landscape smoothness. Secondly, we integrate sample importance into the weighted-geometric-mean MIL model and derive an online approach to maximize the bag likelihood by AnyBoost gradient framework to select the most discriminative layers. Due to the target model consisting of selected discriminative layers, our tracker is more robust while needing fewer features than the traditional Haar-like one and the original DFs one. The experimental results show higher performances of our tracker than those of five state-of-the-art ones on several challenging video sequences.