11,386 research outputs found
Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification
An efficient and effective person re-identification (ReID) system relieves
the users from painful and boring video watching and accelerates the process of
video analysis. Recently, with the explosive demands of practical applications,
a lot of research efforts have been dedicated to heterogeneous person
re-identification (Hetero-ReID). In this paper, we provide a comprehensive
review of state-of-the-art Hetero-ReID methods that address the challenge of
inter-modality discrepancies. According to the application scenario, we
classify the methods into four categories -- low-resolution, infrared, sketch,
and text. We begin with an introduction of ReID, and make a comparison between
Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and
compare existing datasets for performing evaluations, and survey the models
that have been widely employed in Hetero-ReID. We also summarize and compare
the representative approaches from two perspectives, i.e., the application
scenario and the learning pipeline. We conclude by a discussion of some future
research directions. Follow-up updates are avaible at:
https://github.com/lightChaserX/Awesome-Hetero-reIDComment: Accepted by IJCAI 2020. Project url:
https://github.com/lightChaserX/Awesome-Hetero-reI
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
Although the performance of person Re-Identification (ReID) has been
significantly boosted, many challenging issues in real scenarios have not been
fully investigated, e.g., the complex scenes and lighting variations, viewpoint
and pose changes, and the large number of identities in a camera network. To
facilitate the research towards conquering those issues, this paper contributes
a new dataset called MSMT17 with many important features, e.g., 1) the raw
videos are taken by an 15-camera network deployed in both indoor and outdoor
scenes, 2) the videos cover a long period of time and present complex lighting
variations, and 3) it contains currently the largest number of annotated
identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe
that, domain gap commonly exists between datasets, which essentially causes
severe performance drop when training and testing on different datasets. This
results in that available training data cannot be effectively leveraged for new
testing domains. To relieve the expensive costs of annotating new training
samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to
bridge the domain gap. Comprehensive experiments show that the domain gap could
be substantially narrowed-down by the PTGAN.Comment: 10 pages, 9 figures; accepted in CVPR 201
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