Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Histograms of Oriented Gradients for Human Detection
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
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
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
Toward Optimal Kernel-based Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Compressive Structured Light for Recovering Inhomogeneous Participating Media
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
Object tracking using SIFT features and mean shift
Computer Vision and Image Understanding
Visual Tracking via Sparse Representation Based Linear Subspace Model
CIT '09 Proceedings of the 2009 Ninth IEEE International Conference on Computer and Information Technology - Volume 02
Online visual tracking with histograms and articulating blocks
Computer Vision and Image Understanding
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Information Theory
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Independent component analysis of Gabor features for face recognition
IEEE Transactions on Neural Networks
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Intelligent video surveillance is currently one of the most active research topics in computer vision, especially when facing the explosion of video data captured by a large number of surveillance cameras. As a key step of an intelligent surveillance system, robust visual tracking is very challenging for computer vision. However, it is a basic functionality of the human visual system (HVS). Psychophysical findings have shown that the receptive fields of simple cells in the visual cortex can be characterized as being spatially localized, oriented, and bandpass, and it forms a sparse, distributed representation of natural images. In this article, motivated by these findings, we propose an effective appearance model based on sparse coding and apply it in visual tracking. Specifically, we consider the responses of general basis functions extracted by independent component analysis on a large set of natural image patches as features and model the appearance of the tracked target as the probability distribution of these features. In order to make the tracker more robust to partial occlusion, camouflage environments, pose changes, and illumination changes, we further select features that are related to the target based on an entropy-gain criterion and ignore those that are not. The target is finally represented by the probability distribution of those related features. The target search is performed by minimizing the Matusita distance between the distributions of the target model and a candidate using Newton-style iterations. The experimental results validate that the proposed method is more robust and effective than three state-of-the-art methods.