Deep Neural Networks with Inexact Matching for Person Re-Identification
Person Re-Identification is the task of matching images of a person across multiple camera views. Almost all prior approaches address this challenge by attempting to learn the possible transformations that relate the different views of a person from a training corpora. Then, they utilize these transformation patterns for matching a query image to those in a gallery image bank at test time. This necessitates learning good feature representations of the images and having a robust feature matching technique. Deep learning approaches, such as Convolutional Neural Networks (CNN), simultaneously do both and have shown great promise recently.
In this work, we propose two CNN-based architectures for Person Re-Identification. In the first, given a pair of images, we extract feature maps from these images via multiple stages of convolution and pooling. A novel inexact matching technique then matches pixels in the first representation with those of the second. Furthermore, we search across a wider region in the second representation for matching. Our novel matching technique allows us to tackle the challenges posed by large viewpoint variations, illumination changes or partial occlusions. Our approach shows a promising performance and requires only about half the parameters as a current state-of-the-art technique. Nonetheless, it also suffers from false matches at times. In order to mitigate this issue, we propose a fused architecture that combines our inexact matching pipeline with a state-of-the-art exact matching technique. We observe substantial gains with the fused model over the current state-of-the-art on multiple challenging datasets of varying sizes, with gains of up to about 21%.
In this work, we propose two CNN-based architectures for Person Re-Identification. In the first, given a pair of images, we extract feature maps from these images via multiple stages of convolution and pooling. A novel inexact matching technique then matches pixels in the first representation with those of the second. Furthermore, we search across a wider region in the second representation for matching. Our novel matching technique allows us to tackle the challenges posed by large viewpoint variations, illumination changes or partial occlusions. Our approach shows a promising performance and requires only about half the parameters as a current state-of-the-art technique. Nonetheless, it also suffers from false matches at times. In order to mitigate this issue, we propose a fused architecture that combines our inexact matching pipeline with a state-of-the-art exact matching technique. We observe substantial gains with the fused model over the current state-of-the-art on multiple challenging datasets of varying sizes, with gains of up to about 21%.
This paper proposes two CNN models for person re-identification. The novel aspects of the first model are normalized cross correlation (Normalized X-Corr) layer and wider search area for patch matching. The second model is the combination of the Ahmed’s model [1] and the first model. The experimental results show the substantial performance gain over the state-of-the-art deep learning based methods on three benchmark datasets. 
I think the proposed method seems to be somewhat novel and the performance is impressive. However, the evaluation and the writing are clearly rushed and incomplete. Detailed comments are bellows:   Pros ) The performance of the proposed method is impressive. Even with only Normalized X-Corr model, the proposed method achieves substantially higher identification rates on CUHK01 and CUHK03 datasets than state-of-the arts.   The Normalized X-Corr layer is interesting and seems to reasonable if the performance is really gained by this layer (see Cons. comment).   This paper shows that combination of the different deep architectures improves the performance. This is simple idea, but as far as I know, this is not explored except a work [A]. I consider that the work [A] is published after the submission of this paper.   [A] F.Wang, W.Zuo, L.Lin, D.Zhang, L.Zhang, Joint Learning of Single-image and Cross-image Representations for Person Re-identification, In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1288-1296, 2016.  Cons ) The main drawback of this paper is the incomplete baseline comparison. The proposed method has two differences from the Ahmed et al [1]. These are; (1) Normalized X-Corr layer and (2) wider area search. However, this paper does not report the baseline results except the reported results by Ahmed et al. [1]. Therefore, contributions of each part ((1) and (2)) are not understand. For example, the high performance of the proposed method might be comes from the wider area search and the Normalized X-Corr layer might be not contributed to the performance, and vice versa. The authors could compare the performance with the Cross Input Neighborhood layer of Ahmed’s model with wider search area, and Normalized X-Corr layer with smaller area search of Ahmed’s model, for example.   One advantage claimed in this paper “the parameters of the Normalized X-Corr model is about half number compared to the Ahmed’s model (eg. line170-171)” is incorrect. In Fig.3, the size of Normalized X-Corr layer is 12 x 37 x 1500 while the Cross Input Neighborhood layers of Ahmed’s model is 12 x 37 x 1250. Clearly, the parameters of Ahmed’s model is smaller. This paper doesn’t explain how the number of parameters comes from and why it is smaller than that of Ahmed’s model. In my understanding, the depth of Cross Input Neighborhood is comes from 5x5 (search area) x 2 (asymmetric distance) x 25 (feature maps) = 1250. While the depth of Normalized X-Corr is comes from 12x5 (search area) x 1 (symmetric distance) x 25 (feature maps) = 1500. Due to the large search area, the parameters of the proposed model is larger than that of Ahmed’s model.   The VIPeR dataset is the most commonly used dataset for person re-identification. However, the experimental results on this dataset is not reported. It would be better to report the results on VIPeR dataset.   Writing quality of the paper is low:   - Line 21. and 59: What is jumped and which datasets?  - Line 29: “1.1 Problem Definition” can be removed.  - Line 51, 158: What do you mean “images are regular” ? - Line 154: It would be better to explain how did Ahmed et al. set search space.  - Line 183 “they are fused in a 1000 node fully connected layer”: Is it two separate 500 node fully connected layers (Fig. 3) ?  - Line 250: How much did you get speed up by multi-GPU training?  - Line 255, “Normalized X-Corr model give a 3% gain”: It should be “1.63% gain”. (64.73% vs 62.1%).  - Line 275, “a gain of upto 16% over the existing state-of-the art”: Please specify which setting on the CUHK01.  - Some capitalization should be collected or unified. eg.,   “Person Re-Identification” → “person re-identification”   “Dataset” → “dataset”   “Underground Re-identification (GRID)” →“underGround Re-IDenditifcaiton (GRID)”  Supplementary material: - How did you obtain Eq.(1) ? More explanations are need.  - Please use \eqnarray (if you use latex) for Eq.(1-2) and Eq.(5-7) . - Eq.(3) should be removed.  - I do not understand your intention to show the Fig.7-8.