The image flow constraint equation
Computer Vision, Graphics, and Image Processing
Robust model-based motion tracking through the integration of search and estimation
International Journal of Computer Vision
Active vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
SCAAT: incremental tracking with incomplete information
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
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
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Object Localization by Bayesian Correlation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Mean Shift Is a Bound Optimization
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
Gaussian Mean-Shift Is an EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Robust visual tracking for multiple targets
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Short-term audio-visual atoms for generic video concept classification
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Combined Motion and Appearance Models for Robust Object Tracking in Real-Time
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
A practical several moving objects area extraction using a half-cosine function wavelet network
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Audio-visual atoms for generic video concept classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A spatio-spectral algorithm for robust and scalable object tracking in videos
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part III
Adaptive multi-cue tracking by online appearance learning
Neurocomputing
Visual tracking using the Earth Mover's Distance between Gaussian mixtures and Kalman filtering
Image and Vision Computing
A highly repeatable feature detector: improved Harris---Laplace
Multimedia Tools and Applications
Comparison of stochastic filtering methods for 3D tracking
Pattern Recognition
Motion segmentation by model-based clustering of incomplete trajectories
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Iterative filtering of SIFT keypoint matches for multi-view registration in Distributed Video Coding
Multimedia Tools and Applications
Robust Visual Tracking Using an Effective Appearance Model Based on Sparse Coding
ACM Transactions on Intelligent Systems and Technology (TIST)
On collaborative people detection and tracking in complex scenarios
Image and Vision Computing
Robotics and Autonomous Systems
Wavelet-based data reduction for detection of moving objects
Machine Graphics & Vision International Journal
A novel framework for motion segmentation and tracking by clustering incomplete trajectories
Computer Vision and Image Understanding
Bag of features using sparse coding for gender classification
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Tracking vehicles as groups in airborne videos
Neurocomputing
Visual tracking and learning using speeded up robust features
Pattern Recognition Letters
Proceedings of the 2012 ACM Research in Applied Computation Symposium
Color invariant SURF in discriminative object tracking
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part II
Robust visual tracking with discriminative sparse learning
Pattern Recognition
Sparse coding based visual tracking: Review and experimental comparison
Pattern Recognition
Multitarget tracking of pedestrians in video sequences based on particle filters
Advances in 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
Object tracking using learned feature manifolds
Computer Vision and Image Understanding
Neurocomputing
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A scale invariant feature transform (SIFT) based mean shift algorithm is presented for object tracking in real scenarios. SIFT features are used to correspond the region of interests across frames. Meanwhile, mean shift is applied to conduct similarity search via color histograms. The probability distributions from these two measurements are evaluated in an expectation-maximization scheme so as to achieve maximum likelihood estimation of similar regions. This mutual support mechanism can lead to consistent tracking performance if one of the two measurements becomes unstable. Experimental work demonstrates that the proposed mean shift/SIFT strategy improves the tracking performance of the classical mean shift and SIFT tracking algorithms in complicated real scenarios.