On-Line Rigid Object Tracking via Discriminative Feature Classification

Abstract

SUMMARYThis paper proposes an on-line rigid object tracking framework via discriminative object appearance modeling and learning. Strong classifiers are combined with 2D scale-rotation invariant local features to treat tracking as a keypoint matching problem. For on-line boosting, we correspond a Gaussian mixture model (GMM) to each weak classifier and propose a GMM-based classifying mechanism. Meanwhile, selforganizing theory is applied to perform automatic clustering for sequential updating. Benefiting from the invariance of the SURF feature and the proposed on-line classifying technique, we can easily find reliable matching pairs and thus perform accurate and stable tracking. Experiments show that the proposed method achieves better performance than previously reported trackers.

Long-Term Tracking Based on Multi-Feature Adaptive Fusion for Video Target

“…Vast trackers that focus on establishing a robust object appearance model have been explored [3], [4], which can be divided into generative-method and discriminative-method. And we prefer the discriminative-method, for it applies the machine learning to improve the tracking performance.…”