We provide a powerful online discriminative tracking algorithm based on Laplacian Regularized Least Squares (LapRLS) [1]. The tracking algorithm contains two steps. First, a manifold regularized semi-supervised learning method (i.e. LapRLS) is used to learn a robust classifier to detect the target object. Second, an active example selection approach is adopted to automatically select the most informative examples for LapRLS to ensure the high classification confidence of the classifier. The overview of the approach is shown below:

We evaluated this API with 11 state-of-the-art methods on a recent popular benchmark [2] where each tracker is tested on 51 challenging videos (more than 29,000 frames). The state-of-the-art trackers include the TLD tracker [3], tracking with Multiple Instance Learning (MIL) [4], Visual Tracking Decomposition (VTD) [5], the Struck method [6], the Sparsity-based Collaborative Model (SCM) [7], Laplacian Ranking Support Vector Tracking (LRSVT) [8], Compressive Tracking (CT) [9], Structural Part-based Tracking (SPT) [10], Least Soft-threshold Squares Tracking (LSST) [11], Randomized Ensemble Tracking (RET) [12] and tracking with Online Non-negative Dictionary Learning (ONNDL) [13]. Comparison with the state-of-the-art trackers on the comprehensive benchmark demonstrates that our tracking algorithm is more effective and accurate.