Discriminative NBS Tracking

Feb 09, 2026

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This work presents an innovative approach to visual tracking through a highly efficient discriminative image representation using Haar-like features. By leveraging a modified variant of OOMP, we solve the model to achieve rapid object localization through the use of integral images. Our method incorporates a nuanced foreground-background feature decomposition, which effectively utilizes both positive and negative information for enhanced robustness in tracking. Contributed by researchers Ang Li (Nanjing University), Feng Tang (HP Labs), Yanwen Guo (Nanjing University), and Hai Tao (UCSC).

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Discriminative NBS Tracking

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Discriminative NBS Tracking • A highly efficient discriminative image representation using Haar-like features. • Solve the model by a variant of OOMP • Achieve very fast object localization by integral image in visual tracking. … … Foreground Background Feature Decomposition Incorporate both positive and negative information And more robust! Ang Li (Nanjing U), Feng Tang (HP Labs), Yanwen Guo (Nanjing U), Hai Tao (UCSC)