Person Re-identification in Appearance Impaired Scenarios

Gou, Mengran; Zhang, Xikang; Rates-Borras, Angels; Asghari-Esfeden, Sadjad; Sznaier, Mario; Camps, Octavia

doi:10.5244/c.30.48

Cited by 23 publications

(22 citation statements)

References 36 publications

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“…Most of existing methods in person ReID focus on either feature extraction [44,28,23,8,40], or similarity measurement [17,27,22,39]. Person image descriptors commonly used include color histogram [14,17,38], local binary patterns [14], Gabor features [17], and etc., which show certain robustness to the variations of poses, illumination and viewpoints.…”

Section: Related Workmentioning

confidence: 99%

Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification

Chen

Zhang

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

1,118

655

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Person re-identification (ReID) is an important task in wide area video surveillance which focuses on identifying people across different cameras. Recently, deep learning networks with a triplet loss become a common framework for person ReID. However, the triplet loss pays main attentions on obtaining correct orders on the training set. It still suffers from a weaker generalization capability from the training set to the testing set, thus resulting in inferior performance. In this paper, we design a quadruplet loss, which can lead to the model output with a larger inter-class variation and a smaller intra-class variation compared to the triplet loss. As a result, our model has a better generalization ability and can achieve a higher performance on the testing set. In particular, a quadruplet deep network using a margin-based online hard negative mining is proposed based on the quadruplet loss for the person ReID. In extensive experiments, the proposed network outperforms most of the state-of-the-art algorithms on representative datasets which clearly demonstrates the effectiveness of our proposed method.

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Section: Related Workmentioning

confidence: 99%

Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification

Chen

Zhang

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

1,118

655

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“…Typical approaches focus on obtaining view-invariant appearance features [11], and/or learning matching models specific to a given pair of camera views to be matched [20]. Relatively less studied directions include enhancing re-identification using soft-biometrics like attributes [18], height [25,1], shape [1,15], or movement style [14]. Such techniques are likely to be increasingly important when addressing realistic longer-duration home/office data where identity should be estimated correctly despite that people are likely to change clothes.…”

Section: Underpinning Capabilitiesmentioning

confidence: 99%

“…Similarly, tracking benchmarks are typically defined as relatively shortterm problems, where there is limited change in a person's appearance, or long periods without an observation from any camera. In contrast to the practical case where, particularly in smart homes/offices, people may change clothes [14] or spend time out of views of all cameras while in rooms where privacy is expected. Existing work in multitarget multi-camera tracking exploits within-camera tracking, and multi-camera optimisation to find globally coherent estimate of person identities across space and time [26,33].…”

Section: Introductionmentioning

confidence: 99%

A Dataset for Persistent Multi-target Multi-camera Tracking in RGB-D

Layne

Hannuna

Camplani

et al. 2017

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Video surveillance systems are now widely deployed to improve our lives by enhancing safety, security, health monitoring and business intelligence. This has motivated extensive research into automated video analysis. Nevertheless, there is a gap between the focus of contemporary research, and the needs of end users of video surveillance systems. Many existing benchmarks and methodologies focus on narrowly defined problems in detection, tracking, re-identification or recognition. In contrast, end users face higher-level problems such as long-term monitoring of identities in order to build a picture of a person's activity across the course of a day, producing usage statistics of a particular area of space, and that these capabilities should be robust to challenges such as change of clothing. To achieve this effectively requires less widely studied capabilities such as spatio-temporal reasoning about people identities and locations within a space partially observed by multiple cameras over an extended time period. To bridge this gap between research and required capabilities, we propose a new dataset LIMA that encompasses the challenges of monitoring a typical home / office environment. LIMA contains 4.5 hours of RGB-D video from three cameras monitoring a four room house. To reflect the challenges of a realistic practical application, the dataset includes clothes changes and visitors to ensure the global reasoning is a realistic open-set problem. In addition to raw data, we provide identity annotation for benchmarking, and tracking results from a contemporary RGB-D tracker-thus allowing focus on the higher level monitoring problems.

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“…When this assumption does not hold, (see Figure 3.1), up to second order moment information is not sufficient to completely represent relatively complex local regions. Though Fisher Vector encoding feature can mimic a non-Gaussian distribution with GMM and achieve decent results on re-id [40,41], it assumes that the variables at the pixel-level feature are independent from each other. Moreover, the GMM needs a training set to learn its parameters.…”

Section: Mom: Mean Of Moments Featurementioning

confidence: 99%

“…Most of the existing re-id literature focuses on two aspects of the problem: 1) designing viewpoint invariant feature descriptors [37,40,38,42,39,72,41,10,73,74] and/or 2) learning a supervised classifier to alleviate the effect of the variances across the cameras [75,42,36,76,77,48,78,79,80,81]. Recently, deep neural networks have been adopted to learn both the descriptor and classifier simultaneously [63,82,61,64].…”

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confidence: 99%

The empirical moment matrix and its application in computer vision

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(GOG). The images on the first row are from the "probe" view and the second row are from the "gallery" view. In these examples, with the help of higher order moments, moM has better invariance property when the person has a fine-detailed non-Gaussian appearance, e.g., the dot-pattern in column 1, white strip on the backpack in column 2, black strip on a white shirt in column 3 and the stripe-pattern in column 4. . The best result for each metric learning is marked in red and the second best is in blue. The best combination across the whole dataset is mark in bold red. I would also like to thank Professor Mario Sznaier for his guidance and help on all kinds of different fundamental theoretical problems. His endless support makes me feel worried less to pursue the highest goal. I also want to thank Professor Richard Radke for showing me how to collaborate with other team players and the careful counsel on my manuscripts.I would like to thank Professor Jennifer Dy for serving as my committee member and for her insightful comments on my work.I would like to thank Dr. Milind Rajadhyaksha and Dr. Kivanc Kose for their help and wise advice on medical image processing. I'm thrilled to see my knowledge can help cancer patients.I also want to thank Professor Dana Brooks, Professor Mark Niedre and Professor Ningfang Mi for their patience and thoughtful guidance on the organization of NEPSSS seminars. I enjoyed the time with NEPSSS! I would like to thank the supportive faculties and staff in ALERT, especially Professor Michael Silevitch for his insightful advice and the "so what who cares" and Deanna Beirne for her help to maintain the machines and equipment of the project.I want to thank my collaborators from RPI, Dr. Ziyan Wu, Dr. Yang Li and Dr. Srikrishna Karanam for their thoughtful discussions and help on the CLE Airport project.I spent my six year Ph.D. life in one of the best labs, and I would thank all my lab mates, collaborators, mentors and best friends in the Robust System Lab. I would like to thank Binlong, Fei, Walter, Xuefeng, Xiao, Burak, Tom, Oliver, Yongfang, Yin, Ichiro, Zulqarnain, Jing, Weijia, Caglayan, Rasmus, Jose, Sadjad, Tianyu, Wenqian, Bengisu, Angels, Qian, Begum, Xikang, Dong and Yuexi for their sharing and support.Last but the most important, I would like to thank three unique women in my life, my wife Dr. Wenjuan Qin and my daughters Shuhan and Shuxin. They are my source of happiness and power to help and support me through the tough but exciting six years.x Notations R, N set of real number, set of nonnegative integers x, x, X scalar, a vector in R n , a matrix in R m×n x(i) the i-th entry of x X(i, j) the (i, j)-th entry of X X F Frobenius norm of the matrix X ∈ R m×n Embedding local properties of an image, for instance its color intensities or the magnitude and orientation of its gradients, to create a representative feature is a critical component in many computer vision tasks, such as detection, classification, segmentation and tracking. A feature that is representative yet invariant t...

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Person Re-identification in Appearance Impaired Scenarios

Cited by 23 publications

References 36 publications

Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification

Beyond Triplet Loss: A Deep Quadruplet Network for Person Re-identification

A Dataset for Persistent Multi-target Multi-camera Tracking in RGB-D

The empirical moment matrix and its application in computer vision

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