DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset

Gou, Mengran; Karanam, Srikrishna; Liu, WenQian; Camps, Octavia; Radke, Richard J.

doi:10.1109/cvprw.2017.185

Cited by 83 publications

(58 citation statements)

References 34 publications

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“…We select a number of commonly used ReID datasets for use in our evaluation procedures. The primary datasets used in this study are: Market1501 [9], Airport [4], DukeMTMC4ReID [23], LSPS [24], and CUHK03 [25].…”

Section: Evaluation Implementation a Datasetsmentioning

confidence: 99%

An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification

Marchwica

Jamieson

Siva

2018

2018 15th Conference on Computer and Robot Vision (CRV)

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In recent years, a variety of proposed methods based on deep convolutional neural networks (CNNs) have improved the state of the art for large-scale person re-identification (ReID). While a large number of optimizations and network improvements have been proposed, there has been relatively little evaluation of the influence of training data and baseline network architecture. In particular, it is usually assumed either that networks are trained on labeled data from the deployment location (scene-dependent), or else adapted with unlabeled data, both of which complicate system deployment. In this paper, we investigate the feasibility of achieving scene-independent person ReID by forming a large composite dataset for training. We present an indepth comparison of several CNN baseline architectures for both scene-dependent and scene-independent ReID, across a range of training dataset sizes. We show that scene-independent ReID can produce leading-edge results, competitive with unsupervised domain adaption techniques. Finally, we introduce a new dataset for comparing within-camera and across-camera person ReID.

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Section: Evaluation Implementation a Datasetsmentioning

confidence: 99%

An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification

Marchwica

Jamieson

Siva

2018

2018 15th Conference on Computer and Robot Vision (CRV)

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“…Person re-id [41,6,7], which aims to match pedestrian images (or tracklets) from different cameras within a short period, has drawn much attention in the research community. Many datasets [44,22,38,45,13,8,16] have been proposed to promote the research of re-id. However, the videos are captured by just several cameras in nearby locations within a short period.…”

Section: Related Workmentioning

confidence: 99%

“…Whereas there have been a number of public datasets for person re-id [44,22,38,45,13,8,16] and album-based person recognition [43]. But dataset for our task, namely person search with a single portrait, remains lacking.…”

Section: Cast Search In Movies Datasetmentioning

confidence: 99%

“…However, they are substantially different from the problem of person search with one portrait, which we aim to tackle in this work. Specifically, in typical settings of person re-id [44,22,38,45,13,8,16], the queries and the references in the gallery set are usually captured under similar conditions, e.g. from different cameras along a street, and within a short duration.…”

Section: Introductionmentioning

confidence: 99%

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Person Search in Videos with One Portrait Through Visual and Temporal Links

Huang

Lin

2018

Lecture Notes in Computer Science

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In real-world applications, e.g. law enforcement and video retrieval, one often needs to search a certain person in long videos with just one portrait. This is much more challenging than the conventional settings for person re-identification, as the search may need to be carried out in the environments different from where the portrait was taken. In this paper, we aim to tackle this challenge and propose a novel framework, which takes into account the identity invariance along a tracklet, thus allowing person identities to be propagated via both the visual and the temporal links. We also develop a novel scheme called Progressive Propagation via Competitive Consensus, which significantly improves the reliability of the propagation process. To promote the study of person search, we construct a large-scale benchmark, which contains 127K manually annotated tracklets from 192 movies. Experiments show that our approach remarkably outperforms mainstream person re-id methods, raising the mAP from 42.16% to 62.27%. 1

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“…Unlike other datasets that capture image data from public environments such as universities [111,112] [56] [113], shopping locations [51], or publicly accessible spots in transportation gateways [114], the Airport dataset provides data captured from video streams inside the secure area, post the security checkpoint, of a major airport. It is generally very difficult to obtain data from such a camera network, in which configuration settings (e.g., network topology and placement of cameras) are driven by security requirements.…”

Section: Airport Datasetmentioning

confidence: 99%

The empirical moment matrix and its application in computer vision

GOU¹

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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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DukeMTMC4ReID: A Large-Scale Multi-camera Person Re-identification Dataset

Cited by 83 publications

References 34 publications

An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification

An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification

Person Search in Videos with One Portrait Through Visual and Temporal Links

The empirical moment matrix and its application in computer vision

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