Paper:
Learning a Deep Compact Image Representation for Visual Tracking
In this paper, we study the challenging problem of tracking the trajectory of a moving object in a video with possibly very complex background. In contrast to most existing trackers which only learn the appearance of the tracked object online, we take a different approach, inspired by recent advances in deep learning architectures, by putting more emphasis on the (unsupervised) feature learning problem. Specifically, by using auxiliary natural images, we train a stacked denoising autoencoder offline to learn generic image features that are more robust against variations. This is then followed by knowledge transfer from offline training to the online tracking process. Online tracking involves a classification neural network which is constructed from the encoder part of the trained autoencoder as a feature extractor and an additional classification layer. Both the feature extractor and the classifier can be further tuned to adapt to appearance changes of the moving object. Comparison with the state-of-the-art trackers on some challenging benchmark video sequences shows that our deep learning tracker is more accurate while maintaining low computational cost with real-time performance when our MATLAB implementation of the tracker is used with a modest graphics processing unit (GPU).

Review:
This paper presents an application of deep learning to 
      visual tracking. Under the framework of particle filter, this paper builds 
      a likelihood model based on learned image representation from stacked 
      denoising auto-encoder (SDAE), and achieves good results on 11 selected 
      videos compared to previous trackers. The reviewer will evaluate this 
      paper from four aspects below. 1. Quality The main algorithms 
      (particle filter and SDAE) used in this paper are well known and should be 
      correct. Unsupervised feature learning is also well established for image 
      representation. There is no surprise when it works reasonably well for 
      representing tracking targets. It will be good that the authors can 
      present some insights that how the deep learning here help solve any 
      intrinsic problems of visual tracking? For example, is the learned 
      representation robust to occlusions and motion blur? Do the learned 
      features make the visual matching easier, or tracking targets more 
      salient? In addition, the SDAE is online updated while tracking. The 
      reviewer would like to know if this will cause over-fitting and thus 
      tracking drift. 2. Clarity This paper is clearly written and 
      easy to read. Since the source codes of SDAE and particle filter are 
      public available, the results should be easily reproduced. 3. 
      Originality This paper is an incremental work. It shares similar ideas 
      and algorithm framework with [22] of combining feature learning with 
      particle filter. The only difference is that this paper use SDAE for 
      feature learning while [22] uses sparse coding for feature extraction. 
      Although this paper learns features from pixels while [22] learns features 
      from SIFT, no experimental results show the advantages of SDAE in this 
      paper over [22]. The reviewer suggests the authors present thorough 
      comparisons with [22] so as to make explicit the merits of the proposed 
      algorithm. 4. Significance Since the SDAE and particle filter 
      are well known in the community, the direct combination of them bring 
      little new insights towards both deep learning and visual tracking.