Patch-based Keypoints Consensus Voting for Robust Visual Tracking

This paper presents a patch-based keypoints clustering method for long term robust visual tracking. We propose to employ a parallel framework with keypoints matching and estimation for tracking purpose. Patch-based method is implemented in our algorithm to improve the flexibility of system. The template is divided into patches to ensure the spatial constraint of local keypoints. The motion cue of patches is calculated with optical flow for consensus clustering and the outliers are suppressed for the final voting. To eliminate the error, we propose a two-step voting from global to local scope. The effective keypoints in global vote for a center and estimate the patch centers which will be compared with the voting centers from each individial patch keypoints. The final voting is determined by the voting with minimum error, which could robustly reduce the error due to the misclassified outliers. Finally, the experiments will be followed to validate the performance of proposed algorithm on the public benchmark.