EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A Framework for Robust Subspace Learning
International Journal of Computer Vision - Special Issue on Computational Vision at Brown University
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
The Pascal Visual Object Classes (VOC) Challenge
International Journal of Computer Vision
Combined feature evaluation for adaptive visual object tracking
Computer Vision and Image Understanding
Linear Regression for Face Recognition
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
Visual tracking and recognition using appearance-adaptive models in particle filters
IEEE Transactions on Image Processing
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In aerially captured video, low resolution, very small object size and poor image quality may pose tracking as very difficult problem. Based on fundamental principle that single object class lie on a linear subspace, we propose a least squares estimation-based linear model which represents probe candidate as class specific galleries. In this method, tracking is considered as a binary classification problem, which consists of target and background class. Inclusion of background templates improves the discriminative strength of the object model. By projecting target patches on estimated target coefficient vector, all candidates are reconstructed. The squared sum of reconstruction error is used as an observation likelihood function to determine the maximum a posteriori MAP estimation for current frame. To contain the target appearance change during tracking online multiple dictionary learning strategy is proposed. Experiment results of proposed method shows better tracking accuracy and computation when compared to other representative tracking methods.