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Online Discriminative Object Tracking with Local Sparse Representation

Online Discriminative Object Tracking with Local Sparse Representation 2025

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The document presents an online object tracking algorithm that utilizes local sparse representation and classifier learning to enhance robustness against challenges such as background clutter, illumination changes, and occlusions. The proposed method initializes a target object in the first frame, learns sparse codes for image patches using an adaptive dictionary, and employs a linear classifier to distinguish the target from the background. A two-stage tracking algorithm is introduced to mitigate visual drift by combining static and adaptive observation models through particle filtering. Experimental results demonstrate the effectiveness of this approach compared to state-of-the-art methods across various challenging sequences.

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