Abstract
Intelligent CCTV-based surveillance is becoming an essential element in smart cities. Despite the recent explosion of CCTV installed for security purposes, its monitoring still depends on people. Person re-identification is a technique to find an image in disjoint camera views that contains the previously detected pedestrian. Conventional methods for person re-identification used the similarity based on hand-crafted features and their performance heavily relies on lighting or camera angle. In recent years, deep learning-based methods have shown good performance in person re-identification. However, deep learning-based models using two input images have a limitation that they cannot detect similarities and differences between images simultaneously. In this chapter, we propose a model that calculates similarities and differences between images simultaneously by extracting features from the input of three images and reconstructing the extracted feature map.
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References
H. Kim, J. Park, H. Kim, E. Hwang, S. Rho, Robust facial landmark extraction scheme using multiple convolutional neural networks. Multimed. Tools Appl. 78(3), 3221–3238 (2019)
H. Kim, H. Kim, E. Hwang, Real-time facial feature extraction scheme using cascaded networks, in 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), Kyoto, Japan, (IEEE, 2019), pp. 1–7
H.W. Kim, H.J. Kim, S. Rho, E. Hwang, Augmented EMTCNN: A fast and accurate facial landmark detection network. Appl. Sci. 7, 2253 (2020)
H. Chen et al., Deep transfer learning for person re-identification, in 2018 IEEE International Conference on Multimedia Big Data (BigMM), Xi’an, (2018), pp. 1–5. https://doi.org/10.1109/BigMM.2018.8499067
X. Bai, M. Yang, T. Huang, Z. Dou, R. Yu, Y. Xu, Deep-person: Learning discriminative deep features for person re-identification. Pattern Recogn. 98, 107036 (2020)
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in 2016 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 770–778
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556 (2014)
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, Z. Wojna, Rethinking the inception architecture for computer vision, in 2016 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), (2016), pp. 2818–2826
R. Quan, X. Dong, Y. Wu, L. Zhu, Y. Yang, Auto-ReID: Searching for a part-aware ConvNet for person re-identification, in 2019 IEEE International Conference on Computer Vision (ICCV), (2019), pp. 3750–3759
Y. Lin et al., Improving person re-identification by attribute and identity learning. Pattern Recogn. 95, 151–161 (2019)
E. Ahmed, M. Jones, T.K. Marks, An improved deep learning architecture for person re-identification, in 2015 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), (2015), pp. 3908–3916
A. Hermans, L. Beyer, B. Leibe, In defense of the triplet loss for person re-identification, arXiv preprint arXiv:1703.07737 (2017)
F. Schroff, D. Kalenichenko, J. Philbin, FaceNet: A unified embedding for face recognition and clustering, in 2015 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), (2015), pp. 815–823
W. Li, R. Zhao, T. Xiao, X. Wang, DeepReID: Deep filter pairing neural network for person re-identification, in 2014 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), (2014), pp. 152–159
Acknowledgement
This work was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government. [2018-micro-9500, Intelligent Micro-Identification Technology for Music and Video Monitoring].
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Kim, H., Kim, H., Ko, B., Hwang, E. (2021). Person Re-identification Scheme Using Cross-Input Neighborhood Differences. In: Arabnia, H.R., Ferens, K., de la Fuente, D., Kozerenko, E.B., Olivas Varela, J.A., Tinetti, F.G. (eds) Advances in Artificial Intelligence and Applied Cognitive Computing. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-70296-0_61
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DOI: https://doi.org/10.1007/978-3-030-70296-0_61
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