Performance Measures and a Data Set for Multi-target, Multi-camera Tracking

Ristani, Ergys; Solera, Francesco; Zou, Roger S.; Cucchiara, Rita; Tomasi, Carlo

doi:10.1007/978-3-319-48881-3_2

Cited by 2,056 publications

(1,583 citation statements)

References 53 publications

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“…To evaluate the performance of the proposed approach, we selected a subset with crowd scenes from the first 20K frames of training/validation set of camera 8 in DukeMTMC [13]. To be more specific, we selected the frames that contain at least one ground truth bounding box with 40% crowd occlusion but without any bounding box with crowd occlusion over 70%.…”

Section: Discussionmentioning

confidence: 99%

Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset

Huang

Feng

Yan

et al. 2018

Articulated Motion and Deformable Objects

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Abstract. The problem of re-identification of people in a crowd commonly arises in real application scenarios, yet it has received less attention than it deserves. To facilitate research focusing on this problem, we have embarked on constructing a new person re-identification dataset with many instances of crowded indoor and outdoor scenes. This paper proposes a two-stage robust method for pedestrian detection in a complex crowded background to provide bounding box annotations. The first stage is to generate pedestrian proposals using Faster R-CNN and locate each pedestrian using Non-maximum Suppression (NMS). Candidates in dense proposal regions are merged to identify crowd patches. We then apply a bottom-up human pose estimation method to detect individual pedestrians in the crowd patches. The locations of all subjects are achieved based on the bounding boxes from the two stages. The identity of the detected subjects throughout each video is then automatically annotated using multiple features and spatial-temporal clues. The experimental results on a crowded pedestrians dataset demonstrate the effectiveness and efficiency of the proposed method.

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Section: Discussionmentioning

confidence: 99%

Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset

Huang

Feng

Yan

et al. 2018

Articulated Motion and Deformable Objects

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“…The DukeMTMC-reID dataset [47] is a subset of the DukeMTMC dataset [22]. It contains 1812 identities captured by 8 cameras.…”

Section: Datasets and Implementation Detailsmentioning

confidence: 99%

“…The DukeMTMC-VideoReID dataset [31] is a videobased re-ID dataset derived from the DukeMTMC dataset [22]. It contains 2196 tracklets of 702 identities for training, 2636 tracklets of other 702 identities for testing.…”

Section: Datasets and Implementation Detailsmentioning

confidence: 99%

Unsupervised Person Re-identification via Cross-Camera Similarity Exploration

Lin

Yan

et al. 2020

IEEE Trans. on Image Process.

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Person re-identification (re-ID) is an important topic in computer vision. This paper studies the unsupervised setting of re-ID, which does not require any labeled information and thus is freely deployed to new scenarios. There are very few studies under this setting, and one of the best approach till now used iterative clustering and classification, so that unlabeled images are clustered into pseudo classes for a classifier to get trained, and the updated features are used for clustering and so on. This approach suffers two problems, namely, the difficulty of determining the number of clusters, and the hard quantization loss in clustering. In this paper, we follow the iterative training mechanism but discard clustering, since it incurs loss from hard quantization, yet its only product, image-level similarity, can be easily replaced by pairwise computation and a softened classification task. With these improvements, our approach becomes more elegant and is more robust to hyperparameter changes. Experiments on two image-based and video-based datasets demonstrate state-of-the-art performance under the unsupervised re-ID setting.

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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 DukeMTMC4ReID dataset was derived from the DukeMTMC dataset for multi-target tracking [112]. We note that Zheng et al [107] also recently proposed a re-id dataset, called DukeMTMC-reID, based on DukeMTMC.…”

Section: Dukemtmc4reidmentioning

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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Performance Measures and a Data Set for Multi-target, Multi-camera Tracking

Cited by 2,056 publications

References 53 publications

Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset

Robust Pedestrian Detection for Semi-automatic Construction of a Crowded Person Re-Identification Dataset

Unsupervised Person Re-identification via Cross-Camera Similarity Exploration

The empirical moment matrix and its application in computer vision

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