Person re-identification
목차
- 1 Datasets
- 2 Papers
- 2.1 Mahalanobis distance learning for person re-identification
- 2.2 Unsupervised salience learning for person re-identification
- 2.3 Learning mid-level filters for person re-identification
- 2.4 Deepreid: Deep filter pairing neural network for person re-identification
- 2.5 Person re-identification by LOMO(local maximal occurrence) representation and metric learning
- 2.6 Person re-identification using kernel-based metric learning methods
- 2.7 Salient color names for person re-identification
- 2.8 An improved deep learning architecture for person re-identification
- 2.9 Person re-identification by salience matching
- 2.10 Deep metric learning for person re-identification
- 2.11 A survey of approaches and trends in person re-identification
- 2.12 Person re-identification by video ranking
- 2.13 Learning to rank in person re-identification with metric ensembles
- 2.14 Learning a discriminative null space for person re-identification
- 2.15 Learning deep feature representations with domain guided dropout for person re-identification
- 2.16 In Defense of the Triplet Loss for Person Re-Identification
- 3 etc
Datasets
A Systematic Evaluation and Benchmark for Person Re-Identification: Features, Metrics, and Datasets
- 201605. Srikrishna Karanam, Mengran Gou, Ziyan Wu, Angels Rates-Borras, Octavia Camps, Richard J. Radke. arxiv
- cited by 0 !
- multi-shot ranking : 한장만 보는 것이 아니고 여러장을 다 같이 보는것. 비디오등에 적당하다.
이 링크가 기가막힘
- QMUL dataset은 그 유명한 Gong교수의 홈페이지에 있는데, 여기 이런저런 dataset이 몇개 더 있다. activity analysis, human recognition, video summerisation을 하시는 모양. 데이터셋중 QMUL i-LIDS Re-Identification Dataset이 person re-id dataset인것 같다. 289장 3.2M
- PRID2011 > 1G
- V47 은 google docs
State of the art on the MARKET 1501
2017년 최신것까지!
State of the art on the MARS dataset
Papers
구글에서 걍 검색. 2013년 이후 인용 75 이상인것들. (그 이하인것도 두세개 있음. 0도 있다!)
Mahalanobis distance learning for person re-identification
PM Roth, M Hirzer, M Köstinger, C Beleznai… - … Re-Identification, 2014 - Springer [1] Gong의 책중 한 챕터
Unsupervised salience learning for person re-identification
R Zhao, W Ouyang, X Wang - Proceedings of the IEEE …, 2013 - cv-foundation.org [2]
Learning mid-level filters for person re-identification
R Zhao, W Ouyang, X Wang - Proceedings of the IEEE …, 2014 - cv-foundation.org [3]
Deepreid: Deep filter pairing neural network for person re-identification
W Li, R Zhao, T Xiao, X Wang - Proceedings of the IEEE …, 2014 - cv-foundation.org [4]
Person re-identification by LOMO(local maximal occurrence) representation and metric learning
- 논문 홈페이지 소스코드 다운로드 가능
- S Liao, Y Hu, X Zhu, SZ Li - … of the IEEE Conference on Computer …, 2015 - cv-foundation.org [5]
- LOMO feature가 de facto standard인듯. 아래 Learning a discriminative null space for person re-identification 논문에 여러 metric learning algorithm비교한 표가 나오는데 (대부분) LOMO feature로 비교한다.
Retinex algorithm
Retinex considers human lightness and color perception. It aims at producing a color image that is consistent to human observation of the scene. The restored image usually contains vivid color information, especially enhanced details in shadowed regions.
multi-scale Retinex algorithm
SILTP
we also apply the Scale Invariant Local Ternary Pattern (SILTP) [26] descriptor for illumination invariant texture description. SILTP is an improved operator over the well-known Local Binary Pattern (LBP) [37]. In fact, LBP has a nice invariant property under monotonic gray-scale transforms, but it is not robust to image noises. SILTP improves LBP by introducing a scale invariant local comparison tolerance, achieving invariance to intensity scale changes and robustness to image noises.
Bayesian face
learning the distance function corresponds to estimating the covariant matrices \(\Sigma_I\) and \(\Sigma_E\).
I : interpersonal , E : extrapersonal. I : 같은 label의 \(\delta\)x, E : 다른 label의 \(\delta\)x.
Fisher criterion
refFisher criterion is a discriminant criterion function that was first presented by Fisher in 1936. It is defined by the ratio of the between-class scatter to the within-class scatter. By maximizing this criterion, one can obtain an optimal discriminant projection axis. After the sample being projected on to this projection axis, the within-class scatter is minimized and the between-class scatter is maximized.
- \(\large S = \frac{\sigma^2_\text{between}}{\sigma^2_\text{within}} \) 즉, \(S\)가 크면 클수록 구분을 잘 하는 것이다.
- LDA가 이것의 일반화 [6]
- Fisher의 original article에 normally distributed classes 와 equal class covariances 가정을 더하면 LDA [7]
LOMO
(정확히 이해한 것인지 모르겠으나,) 10×10 window를 5 간격으로 움직임. 이미지 크기는 128×48. 각각의 윈도우에 대해 \(\text{SILTP}^{0.3}_{4,3}\), \(\text{SILTP}^{0.3}_{4,5}\), 8×8×8-bin joint HSV histgram을 얻음. 수평으로 같은 위치에 있는 모든 sub-window에 대해 위에서 얻은 세가지 feature의 각 최대값만 취함. 이러면 view point의 change에 대해 어느정도 robust해진다고 주장함. Figure 2가 개략적인 이해에 도움이 됨.
- scale invariant하기 위해 2× average pooling한다. 세번.
- 지나치게 큰 값들을 처리하기 위해 log scaling.
- 최종 feature demension은 26,960
- hand crafted feature를 뽑는다는 점에서, 그닥... feature extraction 관련해서는 의미를 가지기 힘든 논문.
- Reyleigh Quotient? [8]
XQDA
- Cross-view Quadratic Discriminant Analysis
- dimension reduction을 PCA등으로 하면, distance metric learning과 별개로 진행됨. Optimal하지 않다는 뜻.
- 그래서 XQDA로 한다, 이건데. 이건 그냥 일반적인 얘기 아닌가. 식을 보면 LMNN같아보인다.
- QDA와 구분하기 위해 X를 붙인다. QDA는 뭐냐 ref. Quadratic Classifier
- dimension을 어느정도까지 줄여야 하는지도 알 수 있다.
- \(\Sigma_I^{-1} \Sigma_E\)의 eigenvalue가 1보다 큰곳까지만.
- 이 논문의 contribution은 결국 두가지 파트인 것 같다. (dimension reduction + )distance metric learning algorithm의 제안과, feature를 뽑는 방법의 제안.
Person re-identification using kernel-based metric learning methods
F Xiong, M Gou, O Camps, M Sznaier - European conference on computer …, 2014 - Springer [9]
Salient color names for person re-identification
Y Yang, J Yang, J Yan, S Liao, D Yi, SZ Li - European Conference on …, 2014 - Springer [10]
An improved deep learning architecture for person re-identification
E Ahmed, M Jones, TK Marks - Proceedings of the IEEE …, 2015 - cv-foundation.org [11]
Person re-identification by salience matching
R Zhao, W Ouyang, X Wang - Proceedings of the IEEE …, 2013 - cv-foundation.org [12]
Deep metric learning for person re-identification
D Yi, Z Lei, S Liao, SZ Li - Pattern Recognition (ICPR), 2014 …, 2014 - ieeexplore.ieee.org [13]
A survey of approaches and trends in person re-identification
A Bedagkar-Gala, SK Shah - Image and Vision Computing, 2014 - Elsevier [14]
Person re-identification by video ranking
T Wang, S Gong, X Zhu, S Wang - European Conference on Computer …, 2014 - Springer [15]
Learning to rank in person re-identification with metric ensembles
S Paisitkriangkrai, C Shen… - Proceedings of the IEEE …, 2015 - cv-foundation.org [16]
Learning a discriminative null space for person re-identification
L Zhang, T Xiang, S Gong - … of the IEEE Conference on Computer …, 2016 - cv-foundation.org, cited by 44
pdf and code Li Zhang’s page
- distance metric learning에서 보통은 SSS problem(small sample size)을 겪는다. - Deep Learning이 힘을 못쓰는 이유 [1]
- data points of the same classes are collapsed, by a transform, into a single point in a new space
- The null space method, also known as the null Foley-Sammon transfer (NFST)
- We develop a novel semi-supervised learning method in the null space to exploit the abundant unlabelled data to further alleviate the effects of the SSS problem.
- Recent person re-id works
- Design invariant and discriminant features
- Learning robust and discriminative distance metrics or sub-spaces for matching people across views
- Learning distance metrics
- Learning discriminative subspaces
- Deep Learning
- Dictionary learning for sparse coding [25, 16] 뭐냐 이건 또. unsupervised라는데.
- 일단 null space로 옮기고, 나중에 test는 해당 null space상에서 euclidean distance로 한다.
- LDA(Linear Discriminant Analysis)는 FST로도 알려져 있다. Foley-Sammon transform
- KEY IDEA : Re-learning the projection matrix runs iteratively till the average distance for the k-nearest-neighbours stop decreasing.
라벨된 데이터로 학습된 \(W^0\)로 unlabelled data projection하고, k-nn으로 돌린 후 상위 \(f\) percent만 갈라서 pseudo class P를 만든다. P를 labelled data에 포함시키고 위 과정 반복.
- 속도가 약간 느린듯.
Learning deep feature representations with domain guided dropout for person re-identification
- T Xiao, H Li, W Ouyang, X Wang - Proceedings of the IEEE …, 2016 - cv-foundation.org [17], cited by 39
- github
- net size 기술을 table로 했는데 한눈에 알아보기 쉽고 좋은듯
- inception net이 뭐냐
- person re-id target이지만 다른곳으로도 확장가능할 것이다.
- BN-Inception [16, 37] modules??
- [16] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. ICML, 2015.
- [37] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. In CVPR, 2015.
- Our method assigns each neuron a specific dropout rate for each domain according to its effectiveness on that domain
- 종류는 두가지 : deterministic, stochastic
- baseline(모든 도메인에 대해 그냥 학습. 일반적인 학습)만든 뒤에 deterministic domain guided dropout(DGD)으로 전체 domain에 대해 학습하고 마지막으로 specific domain에 대해 stochastic DGD로 fine tuning한다.(마지막단계는 optional)
- deterministic DGD하기 위해서 각 뉴런이 특정 도메인에서 얼마나 중요한지 모두 측정해야 한다. 측정 이후 domain별로 학습.
- imagenet으로 pretrain된 net은 적절하지 않다고 판단해서 나름 만듦. re-id는 직사각형, imagenet은 detail이 좋은 데이터 - 이게 이유라고 하는데.... imagenet으로 해보고 안되니까 만들었을듯 ㅋ
In Defense of the Triplet Loss for Person Re-Identification
triplet loss
이 외에도 엄청많음... 그냥 정리 포기.
뜨는필드인가. 왜이렇게 논문이 많아.
etc
http://www.i.kyushu-u.ac.jp/~matsukawa/ReID.html
blog comments powered by Disqus
- ↑ note: 이것때문이라면, learning rate조절부분에서 뭔가 해줄것이 없나. 조금씩 변화가 아니라 두 쌍의 중점을 배우는 식으로. 가장 안맞는 것을 미리 아는 방법이 없을까.