Robust Visual Tracking via Weighted Extreme Learning Machine

In this paper, a model based on weighted extreme learning machine (weighted ELM) is proposed for visual tracking. The weighted ELM considers different class distributions both of the positive and negative classes, where extra weights are utilized in the framework. The proposed model simultaneously trains a certain number of weighted ELMs with different feature blocks. Moreover, the weighted multiple instance learning (weighted MI-L) scheme is utilized in choosing the network with the greatest feature block, which has the most discriminative ability. In addition, a special method of calculating global output weights in weighted ELMs is introduced into the tracking framework. Experimental results on some video clips illustrate the good tracking ability of our tracker in many challenging circumstances.

[1]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[2]  Guang-Bin Huang,et al.  Extreme Learning Machine for Multilayer Perceptron , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Haibin Ling,et al.  Real time robust L1 tracker using accelerated proximal gradient approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Yiqiang Chen,et al.  Weighted extreme learning machine for imbalance learning , 2013, Neurocomputing.

[5]  Kaihua Zhang,et al.  Real-time visual tracking via online weighted multiple instance learning , 2013, Pattern Recognit..

[6]  Ming-Hsuan Yang,et al.  Object Tracking Benchmark , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Rui Caseiro,et al.  Exploiting the Circulant Structure of Tracking-by-Detection with Kernels , 2012, ECCV.

[8]  Jianping Yin,et al.  Boosting weighted ELM for imbalanced learning , 2014, Neurocomputing.

[9]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[10]  Fuchun Sun,et al.  Multitask Extreme Learning Machine for Visual Tracking , 2013, Cognitive Computation.