Object Recognition with Features Inspired by Visual Cortex
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
Robust visual tracking based on simplified biologically inspired features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose a novel algorithm for object tracking based on biologically inspired model (BIM) with hierarchical feedback architectures. The system integrates a support vector machine (SVM) classifier into a patch-based tracker. The template object is represented by a set of patches in the bottom layer of BIM. Each patch is tracked individually and a candidate position of object center is predicted based on the position of the corresponding patch in the current frame. Then the top layer feature of the candidate area is passed to the SVM classifier, and the top SVM score determines which patch prediction is reliable. Numerous experimental results validate that the proposed method is quite effective and robust to illumination changes, partial occlusions and slight appearance changes.