The success plots for eight challenge attributes: illumination variation, deformation, out-of-plane rotation, in-plane rotation, scale variation, low resolution, occlusion, motion blur.

<p>The success plots for eight challenge attributes: illumination variation, deformation, out-of-plane rotation, in-plane rotation, scale variation, low resolution, occlusion, motion blur.</p

Architecture of the two-layer CPGDN model [55] with a full connection layer.

<p>The first layer is CRBM, and the second layer is CPGBM with two mixture components. <i>z</i> is a gating mechanism and its value is binary variable. <i>z</i> and are complementary, i.e., . We use the first component of CPGBM as the input of a full connection layer.</p

Overview of the proposed CPGDN-based tracking method.

<p>Overview of the proposed CPGDN-based tracking method.</p

The precision and success plots of quantitative comparison for the 50 sequences in the CVPR2013 tracking benchmark [30].The performance score of each tracker is shown in the legend.

<p>The proposed CPGDN-based tracker (named CPGDN) ranks 4th in precision plots and 3th in success plots respectively.</p

Quantitative comparison on the center distance error per frame for the four image sequences from [30].

<p>Quantitative comparison on the center distance error per frame for the four image sequences from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0161808#pone.0161808.ref030" target="_blank">30</a>].</p