Robust adaptive control
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
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Hyperplane Approximation for Template Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lucas-Kanade 20 Years On: A Unifying Framework
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
ACM Computing Surveys (CSUR)
Robust tracking with motion estimation and local Kernel-based color modeling
Image and Vision Computing
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Robust object tracking with background-weighted local kernels
Computer Vision and Image Understanding
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Discriminative spatial attention for robust tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Probabilistic tracking in joint feature-spatial spaces
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
3D SSD tracking from uncalibrated video
SCVMA'04 Proceedings of the First international conference on Spatial Coherence for Visual Motion Analysis
Scribble Tracker: A Matting-Based Approach for Robust Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust object tracking via sparsity-based collaborative model
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Struck: Structured output tracking with kernels
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Real-time compressive tracking
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
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In this paper, we propose a constrained optimization approach to improving both the robustness and accuracy of kernel tracking which is appropriate for real-time video surveillance due to its low computational load. Typical tracking with histogram-wise matching provides robustness but has insufficient accuracy, because it does not involve spatial information. On the other hand, tracking with pixel-wise matching achieves accurate performance but is not robust against deformation of a target object. To find the best compromise between robustness and accuracy, in our paper, we combine histogram-wise matching and pixel-wise template matching via constrained optimization problem. Firstly, we propose a novel weight image representing both the probability of foreground and the degree of similarity between the template and a candidate target image. The weight image is used to formulate an objective function for the histogram-wise weight matching. Then the pixel-wise matching is formulated as a constrained optimization problem using the result of the histogram-wise weight matching. In consequence, the proposed approach optimizes pixel-wise template similarity (for accuracy) under the constraints of histogram-wise feature similarity (for robustness). Experimental results show the combined effects, and demonstrate that our method outperforms recent tracking algorithms in terms of robustness, accuracy, and computational cost.