Robust tracking via weighted online extreme learning machine

A Robust Online Sequential Extreme Learning Machine

Robust Tracking with Weighted Online Structured Learning

Robust visual tracking requires constant update of the target appearance model, but without losing track of previous appearance information. One of the difficulties with the online learning approach to this problem has been a lack of flexibility in the modelling of the inevitable variations in target and scene appearance over time. The traditional online learning approach to the problem treats ...

Robust Tracking with Weighted Online Structured Learning: Appendix

Rui Yao, Qinfeng Shi, Chunhua Shen, Yanning Zhang, and Anton van den Hengel 1 School of Computer Science, Northwestern Polytechnical University, China 2 School of Computer Science, The University of Adelaide, Australia This appendix contains two parts. In Section 1, we present the full proof of Proposition 1, Theorem 1 and Corollary 1, which are appeared in main body of paper. We show more expe...

Part-Based Visual Tracking via Online Weighted P-N Learning

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of...

A Robust and Regularized Extreme Learning Machine

In a moment when the study of outlier robustness within Extreme Learning Machine is still in its infancy, we propose a method that combines maximization of the hidden layer’s information transmission, through Batch Intrinsic Plasticity (BIP), with robust estimation of the output weights. This method named R-ELM/BIP generates a reliable solution in the presence of corrupted data with a good gene...