A guided tour of computer vision
A guided tour of computer vision
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
Resolving Motion Correspondence for Densely Moving Points
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
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)
Non-parametric Similarity Measures for Unsupervised Texture Segmentation and Image Retrieval
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Real-Time Tracking Using Trust-Region Methods
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Target Tracking in Infrared Image Sequences Using Diverse AdaBoostSVM
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
ACM Computing Surveys (CSUR)
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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In this paper, we present a new perspective on object tracking based on Bayesian Ying-Yang learning theory and five action circling. During the tracking procedure, the A5 circling must be kept well balanced. Each action should be neither too weak to sustain the system nor too loaded to jam the circling. For object tracking, the A5 paradigm is explained as follow: i) object acquirement and initialization; ii) object representation and description; iii) hypothesis measurement; iv) optimization and estimation; v) object assessment and location. Our extensive experiments show that the proposed novel framework performs robustly in a large variety of image sequences. Additional, a new insight is given on the Bayesian Ying-Yang system, best harmony learning, and five action circling theory from a perspective of object tracking system.