EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Random projection in dimensionality reduction: applications to image and text data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Database-friendly random projections: Johnson-Lindenstrauss with binary coins
Journal of Computer and System Sciences - Special issu on PODS 2001
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Very sparse random projections
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-taught learning: transfer learning from unlabeled data
Proceedings of the 24th international conference on Machine learning
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Semi-supervised On-Line Boosting for Robust Tracking
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Tracking with Online Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Tracking and Vehicle Classification via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification from Random Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time visual tracking using compressive sensing
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Decoding by linear programming
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Struck: Structured output tracking with kernels
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Real-time annotation of video objects on tablet computers
Proceedings of the 11th International Conference on Mobile and Ubiquitous Multimedia
Sparse coding based visual tracking: Review and experimental comparison
Pattern Recognition
Using appearance re-matching to improve real-time compressive tracking
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
An improved real-time compressive tracking method
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
A multiple face detection and tracking system based on TLD
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Non-rigid target tracking based on 'flow-cut' in pair-wise frames with online hough forests
Proceedings of the 21st ACM international conference on Multimedia
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
Real-time visual tracking based on an appearance model and a motion mode
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
Visual tracking based on Distribution Fields and online weighted multiple instance learning
Image and Vision Computing
Visual tracking via weakly supervised learning from multiple imperfect oracles
Pattern Recognition
Computer Vision and Image Understanding
Abrupt motion tracking using a visual saliency embedded particle filter
Pattern Recognition
Take your eyes off the ball: Improving ball-tracking by focusing on team play
Computer Vision and Image Understanding
Collaborative object tracking model with local sparse representation
Journal of Visual Communication and Image Representation
Multi-Target Tracking by Online Learning a CRF Model of Appearance and Motion Patterns
International Journal of Computer Vision
Hi-index | 0.00 |
It is a challenging task to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. Existing online tracking algorithms often update models with samples from observations in recent frames. While much success has been demonstrated, numerous issues remain to be addressed. First, while these adaptive appearance models are data-dependent, there does not exist sufficient amount of data for online algorithms to learn at the outset. Second, online tracking algorithms often encounter the drift problems. As a result of self-taught learning, these mis-aligned samples are likely to be added and degrade the appearance models. In this paper, we propose a simple yet effective and efficient tracking algorithm with an appearance model based on features extracted from the multi-scale image feature space with data-independent basis. Our appearance model employs non-adaptive random projections that preserve the structure of the image feature space of objects. A very sparse measurement matrix is adopted to efficiently extract the features for the appearance model. We compress samples of foreground targets and the background using the same sparse measurement matrix. The tracking task is formulated as a binary classification via a naive Bayes classifier with online update in the compressed domain. The proposed compressive tracking algorithm runs in real-time and performs favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.