Locality-Aware Crowd Counting

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Locality-Aware Crowd Counting
Title:
Locality-Aware Crowd Counting
Journal Title:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Publication Date:
03 February 2021
Citation:
Zhou, J. T., Zhang, L., Jiawei, D., Peng, X., Fang, Z., Xiao, Z., & Zhu, H. (2021). Locality-Aware Crowd Counting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. doi:10.1109/tpami.2021.3056518
Abstract:
Imbalanced data distribution in crowd counting datasets leads to severe under-estimation and over-estimation problems, which has been less investigated in existing works. In this paper, we tackle this challenging problem by proposing a simple but effective locality-based learning paradigm to produce generalizable features by alleviating sample bias. Our proposed method is locality-aware in two aspects. First, we introduce a locality-aware data partition (LADP) approach to group the training data into different bins via locality-sensitive hashing. As a result, a more balanced data batch is then constructed by LADP. To further reduce the training bias and enhance the collaboration with LADP, a new data augmentation method called locality-aware data augmentation (LADA) is proposed where the image patches are adaptively augmented based on the loss. The proposed method is independent of the backbone network architectures, and thus could be smoothly integrated with most existing deep crowd counting approaches in an end-to-end paradigm to boost their performance. We also demonstrate the versatility of the proposed method by applying it for adversarial defense. Extensive experiments verify the superiority of the proposed method over the state of the arts.
License type:
Publisher Copyright
Funding Info:
This work was supported by the Fundamental Research Funds for the Central Universities under Grant YJ201949, by programmatic grant no. A18A1b0045 and no. A1687b0033 from the Singapore government’s Research, Innovation and Enterprise 2020 plan (Advanced Manufacturing and Engineering domain), and by NFSC under Grant 61702182, U19A2081, 61625204, and 61836006.
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
0162-8828
2160-9292
1939-3539
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