A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification

Liu, Kan; Ma, Bingpeng; Zhang, Wei; Huang, Rui

doi:10.1109/iccv.2015.434

Cited by 183 publications

(179 citation statements)

References 29 publications

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“…A typical Re-ID algorithm is based on appearance modeling and matching [38,39]. Appearance modeling often uses low-level features such as color, texture, gradient or a combination thereof to build more discriminative appearance descriptors [37,38]. Many successful Re-ID algorithms have been proposed for special target Re-ID systems [37][38][39][40], such as pedestrians and vehicles.…”

Section: Related Workmentioning

confidence: 99%

“…Appearance modeling often uses low-level features such as color, texture, gradient or a combination thereof to build more discriminative appearance descriptors [37,38]. Many successful Re-ID algorithms have been proposed for special target Re-ID systems [37][38][39][40], such as pedestrians and vehicles. Liu et al [37] exploited a spatio-temporal body-action model by using Fisher vector learning to solve the large appearance variation problem presented by a pedestrian.…”

Section: Related Workmentioning

confidence: 99%

“…Re-ID has been intensively studied for stationary inter-camera target associations [37] for long-term object tracking. A typical Re-ID algorithm is based on appearance modeling and matching [38,39].…”

Section: Related Workmentioning

confidence: 99%

“…Many successful Re-ID algorithms have been proposed for special target Re-ID systems [37][38][39][40], such as pedestrians and vehicles. Liu et al [37] exploited a spatio-temporal body-action model by using Fisher vector learning to solve the large appearance variation problem presented by a pedestrian. Zapletal et al [38] proposed an approach based on a linear regression model using color histograms and histograms of oriented gradients for vehicle re-identification in a multiple cameras scenario.…”

Section: Related Workmentioning

confidence: 99%

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A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos

Chen

Pennisi

et al. 2018

Remote Sensing

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Multi-Object Tracking (MOT) in airborne videos is a challenging problem due to the uncertain airborne vehicle motion, vibrations of the mounted camera, unreliable detections, changes of size, appearance and motion of the moving objects and occlusions caused by the interaction between moving and static objects in the scene. To deal with these problems, this work proposes a four-stage hierarchical association framework for multiple object tracking in airborne video. The proposed framework combines Data Association-based Tracking (DAT) methods and target tracking using a compressive tracking approach, to robustly track objects in complex airborne surveillance scenes. In each association stage, different sets of tracklets and detections are associated to efficiently handle local tracklet generation, local trajectory construction, global drifting tracklet correction and global fragmented tracklet linking. Experiments with challenging airborne videos show significant tracking improvement compared to existing state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos

Chen

Pennisi

et al. 2018

Remote Sensing

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“…On the other hand, assuming the availability of multiple shots of a target person available, multi-shot re-ID also attracted the interests of many researchers [50,51,37]. Furthermore, by extending multiple shots to a short video clip, Wang et al [52] started the work on video-based re-ID and drew a lot attentions from other researchers [53,54,55,56]. Around the same time, several unsupervised re-ID methods [57,58,59,60] were proposed to tackle the challenge of ground truth labeling for person re-ID.…”

mentioning

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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Learning to rank: An intelligent system for person reidentification

Khan

Haq

Khan

et al. 2022

Int J of Intelligent Sys

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Person reidentification (P‐Reid) is an emerging research domain in the field of information retrieval that has gained exponential growth due to its wide range of applications in pedestrian tracking and crime prevention. The primary goal of P‐Reid is to recognize a person based on previous appearance in multiview surveillance videos. The mainstream approaches apply fully supervised learning techniques that have poor scalability when deployed in complex real‐world scenes, due to the overfitting problem, caused by the lack of sufficient annotated data. Further, optimization of these models for unlabeled data in real‐time surveillance is a challenging task. To tackle these issues, an intelligent framework (LR‐Net) is proposed, consisting of three tiers including fine‐tuning (FT), siamese network (SN), and fusion strategy (FS). In the first tier, a deep learning model is fine‐tuned for P‐Reid that can handle both labeled and unlabeled data. Next, with the assistance of transfer learning, an SN is proposed that has a strong discriminative capability in terms of similarity between a pair of images. Finally, a learning‐to‐rank strategy is applied to optimize the learning capability of the SN, in which a triplet network extracts spatial‐temporal patterns from unlabeled samples. In addition, a bayesian fusion model (BFM) is introduced to integrate the spatiotemporal and visual features, which yields 4.4%, 9.3%, and 0.8% improvement in the matching score over Market‐1501, DukeMCMT‐reID, and CUHK03 data sets, respectively. The conducted experiments and ablation study on the benchmark data sets empirically validate the proposed system, which obtains a high Rank‐1 score as compared with the state‐of‐the‐art (SOTA) methods.

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A Spatio-Temporal Appearance Representation for Video-Based Pedestrian Re-Identification

Cited by 183 publications

References 29 publications

A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos

A Hierarchical Association Framework for Multi-Object Tracking in Airborne Videos

The empirical moment matrix and its application in computer vision

Learning to rank: An intelligent system for person reidentification

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