text: In this paper, the authors propose a novel tracking loss to convert the RPN to a tracker.  The internal structure of top layer features of RPN is exploited to treat feature points discriminatively.  In addition, the proposed compression network speeds up the tracking algorithm.  The experimental results on the VOT2016 dataset demonstrate its efficiency in tracking.  This work is the combination of Faster R-CNN (Ren et al. PAMI 2015) and tracking-by-detection framework.  The main contributions proposed in this paper are new tracking loss, network compression and results.  There are numerous concerns with this work: 1.	The new tracking loss shown in equation 2 is similar with the original Faster R-CNN loss shown in equation 1.  The only difference is to replace the regression loss with a predefined mask selection loss,  which is of little sense that the feature processing can be further fulfilled through one-layer CNN.  The empirical operation shown in figure 2 seems arbitrary and lack of theoretical explanation.  There is no insight of why doing so.  Simply showing the numbers in table 1 does not imply the necessity,  which ought to be put in the experiment sections.  2.	The network compression is engineering and lack insight as well.  To remove part of the CNN and retrain is a common strategy in the CNN compression methods [a] [b].  There is a lack of discussion with the relationship with prior arts. 3.	The organization is not clear.  Section 3.4 should be set in the experiments  and Section 3.5 should be set at the beginning of the algorithm.  The description of the network compression is not clear enough, especially the training details.   Meanwhile, the presentation is hard to follow.  There is no clear expression of how the tracker performs in practice. 4.	In addition, VOT 2016, the method should evaluate on the OTB dataset with the following trackers [c] [d]. 5.	The evaluation is not fair.  In Sec 6, the authors indicate that MDNet runs at 1FPS while the proposed tracker runs at 1.6FPS.  However, MDNet is based on Matlab  and the proposed tracker is based on C++ (i.e., Caffe). Reference:[a] On Compressing Deep Models by Low Rank and Sparse Decomposition. Yu et al. CVPR 2017. [b] Designing Energy-Efficient Convolutional Neural Network Using Energy-Aware Pruning. Yang et al. CVPR 2017. [c] ECO: Efficient Convolution Operators for Tracking. Danelljan et al. CVPR 2017. [d] Multi-Task Correlation Particle Filter For Robust Object Tracking. Zhang et al. CVPR 2017.