Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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
Color-Based Tracking of Heads and Other Mobile Objects at Video Frame Rates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Real-time tracking of image regions with changes in geometry and illumination
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
WACV '96 Proceedings of the 3rd IEEE Workshop on Applications of Computer Vision (WACV '96)
Real Time Face and Object Tracking as a Component of a Perceptual User Interface
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Combining Kalman Filtering and Mean Shift for Real Time Eye Tracking under Active IR Illumination
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
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
Sparse Bayesian Learning for Efficient Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Global Kernel Density Mode Seeking with Application to Localisation and Tracking
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Real time hand tracking by combining particle filtering and mean shift
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast occluded object tracking by a robust appearance filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new diamond search algorithm for fast block-matching motion estimation
IEEE Transactions on Image Processing
A novel four-step search algorithm for fast block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
A new three-step search algorithm for block motion estimation
IEEE Transactions on Circuits and Systems for Video Technology
Color in image and video processing: most recent trends and future research directions
Journal on Image and Video Processing - Color in Image and Video Processing
Robust object tracking with background-weighted local kernels
Computer Vision and Image Understanding
Sputnik Tracker: Having a Companion Improves Robustness of the Tracker
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
High-dimensional statistical measure for region-of-interest tracking
IEEE Transactions on Image Processing
Adaptive pyramid mean shift for global real-time visual tracking
Image and Vision Computing
Binocular Based Moving Target Tracking for Mobile Robot
ICIRA '09 Proceedings of the 2nd International Conference on Intelligent Robotics and Applications
Multi-view object matching and tracking using canonical correlation analysis
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Visual tracking by adaptive kalman filtering and mean shift
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
Tracking video objects with feature points based particle filtering
Multimedia Tools and Applications
Space-Efficient approximation scheme for circular earth mover distance
LATIN'12 Proceedings of the 10th Latin American international conference on Theoretical Informatics
Incorporation of GPS and IP camera for people tracking
GPS Solutions
Computer Vision and Image Understanding
Weighted attentional blocks for probabilistic object tracking
The Visual Computer: International Journal of Computer Graphics
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Visual tracking has been a challenging problem in computer vision over the decades. The applications of visual tracking are far-reaching, ranging from surveillance and monitoring to smart rooms. Mean-shift tracker, which gained attention recently, is known for tracking objects in a cluttered environment. In this work, we propose a new method to track objects by combining two well-known trackers, sum-of-squared differences (SSD) and color-based mean-shift (MS) tracker. In the proposed combination, the two trackers complement each other by overcoming their respective disadvantages. The rapid model change in SSD tracker is overcome by the MS tracker module, while the inability of MS tracker to handle large displacements is circumvented by the SSD module. The performance of the combined tracker is illustrated to be better than those of the individual trackers, for tracking fast-moving objects. Since the MS tracker relies on global object parameters such as color, the performance of the tracker degrades when the object undergoes partial occlusion. To avoid adverse effects of the global model, we use MS tracker to track local object properties instead of the global ones. Further, likelihood ratio weighting is used for the SSD tracker to avoid drift during partial occlusion and to update the MS tracking modules. The proposed tracker outperforms the traditional MS tracker as illustrated.