BV-Person: A Large-scale dataset for bird-view person re-identification

• Main Authors: YAN, Cheng, PANG, Guansong, WANG, Lei, JIAO, Jile, FENG, Xuetao, SHEN, Chunhua, LI, Jingjing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Datasets and evaluation; Image and video retrieval; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/7312; https://ink.library.smu.edu.sg/context/sis_research/article/8315/viewcontent/BV_Person_ICCV_2021_av_oa.pdf
• Institution: Singapore Management University

Person Re-IDentification (ReID) aims at re-identifying persons from non-overlapping cameras. Existing person ReID studies focus on horizontal-view ReID tasks, in which the person images are captured by the cameras from a (nearly) horizontal view. In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i.e., an elevated view of an object from above. The task is important because there are a large number of video surveillance cameras capturing persons from such an elevated view at public places. However, it is a challenging task in that the images from the bird view (i) provide limited person appearance information and (ii) have a large discrepancy compared to the persons in the horizontal view. We aim to facilitate the development of person ReID from this line by introducing a large-scale real-world dataset for this task. The proposed dataset, named BV-Person, contains 114k images of 18k identities in which nearly 20k images of 7.4k identities are taken from the bird's-eye view. We further introduce a novel model for this new ReID task. Large-scale experiments are performed to evaluate our model and 11 current state-of-the-art ReID models on BV-Person to establish performance benchmarks from multiple perspectives. The empirical results show that our model consistently and substantially outperforms the state-of-the-art models on all five datasets derived from BV-Person. Our model also achieves state-of-the-art performance on two general ReID datasets. The BV-Person dataset is available at: https://git.io/BVPerson