{"type":"ScholarlyArticle","title":"Cascaded SR-GAN for Scale-Adaptive Low Resolution Person Re-identification","authors":[{"name":"Zheng Wang","slug":"zheng-wang","given":"Zheng","family":"Wang","affiliation":"National Institute of Informatics , Japan"},{"name":"Mang Ye","slug":"mang-ye","given":"Mang","family":"Ye","affiliation":"Hong Kong Baptist University , China"},{"name":"Fan Yang","slug":"fan-yang","given":"Fan","family":"Yang","affiliation":"The University of Tokyo , Japan"},{"name":"Xiang Bai","slug":"xiang-bai","given":"Xiang","family":"Bai","affiliation":"Huazhong University of Science of Technology , China"},{"name":"Shin'ichi Satoh","slug":"shin-ichi-satoh","given":"Shin'ichi","family":"Satoh","affiliation":"National Institute of Informatics , Japan; The University of Tokyo , Japan"}],"abstract":"Person re-identification (REID) is an important task in video surveillance and forensics applications. Most of previous approaches are based on a key assumption that all person images have uniform and sufficiently high resolutions. Actually, various low-resolutions and scale mismatching always exist in open world REID. We name this kind of problem as Scale-Adaptive Low Resolution Person Re-identification (SALR-REID). The most intuitive way to address this problem is to increase various low-resolutions (not only low, but also with different scales) to a uniform high-resolution. SR-GAN is one of the most competitive image super-resolution deep networks, designed with a fixed upscaling factor. However, it is still not suitable for SALR-REID task, which requires a network not only synthesizing high-resolution images with different upscaling factors, but also extracting discriminative image feature for judging person’s identity. (1) To promote the ability of scale-adaptive upscaling, we cascade multiple SRGANs in series. (2) To supplement the ability of image feature representation, we plug-in a reidentification network. With a unified formulation, a Cascaded Super-Resolution GAN (CSR-GAN) framework is proposed. Extensive evaluations on two simulated datasets and one public dataset demonstrate the advantages of our method over related state-of-the-art methods.","doi":"10.24963/ijcai.2018/541","url":"https://ijcai.org/proceedings/2018/541","pdf":"https://ijcai.org/proceedings/2018/0541.pdf","markdown":"https://ijcai.org/proceedings/2018/541.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/541","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Multidisciplinary Topics and Applications","track":"Main track","number":541,"pages":{"first":3891,"last":3897},"keywords":["Computer Vision: Video: Events, Activities and Surveillance","Multidisciplinary Topics and Applications: Security and Privacy"],"videos":[],"references":[{"position":1,"raw":"[An et al., 2015] Le An, Mehran Kafai, Songfan Yang, and Bir Bhanu. Person re-identification with reference descriptor. IEEE Trans. Circuits Syst. Video Technol., 2015.","title":"Person re-identification with reference descriptor","authors":"An","year":"2015","venue":"IEEE Trans. Circuits Syst. Video Technol","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Baltieri et al., 2011] Davide Baltieri, Roberto Vezzani, and Rita Cucchiara. 3dpes: 3d people dataset for surveillance and foren- sics. In Joint ACM workshop on Human gesture and behavior understanding, 2011.","title":"3dpes: 3d people dataset for surveillance and forensics","authors":"Davide Baltieri, Roberto Vezzani, Rita Cucchiara","year":"2011","venue":"Joint ACM workshop on Human gesture and behavior understanding","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2015] Jiaxin Chen, Zhaoxiang Zhang, and Yunhong Wang. Relevance metric learning for person re-identification by exploiting listwise similarities. IEEE Trans. Image Proc., 2015.","title":"Relevance metric learning for person re-identification by exploiting listwise similarities","authors":"Jiaxin Chen, Zhaoxiang Zhang, Yunhong Wang","year":"2015","venue":"IEEE Trans. Image Proc","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cheng et al., 2011] Dong Seon Cheng, Marco Cristani, Michele Stoppa, Loris Bazzani, and Vittorio Murino. Custom pictorial structures for re-identification. In BMVC, 2011.","title":"Custom pictorial structures for re-identification","authors":"Marco Dong Seon Cheng, Michele Cristani, Loris Stoppa, Vittorio Bazzani, Murino","year":"2011","venue":"BMVC","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dong et al., 2016] Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. Image super-resolution using deep convolu- tional networks. IEEE Trans. Pattern Anal. Mach. Intell., 2016.","title":"Image super-resolution using deep convolutional networks","authors":"Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang","year":"2016","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014.","title":"Generative adversarial nets","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gray et al., 2007] Douglas Gray, Shane Brennan, and Hai Tao. Evaluating appearance models for recognition, reacquisition, and tracking. In IEEE International Workshop on Performance Eval- uation for Tracking and Surveillance (PETS), 2007.","title":"Evaluating appearance models for recognition, reacquisition, and tracking","authors":"Douglas Gray, Shane Brennan, Hai Tao","year":"2007","venue":"IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS)","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Jiang et al., 2017] Junjun Jiang, Jiayi Ma, Chen Chen, Xinwei Jiang, and Zheng Wang. Noise robust face image super- resolution through smooth sparse representation. IEEE Trans. Cybern., 2017.","title":"Noise robust face image superresolution through smooth sparse representation","authors":"Junjun Jiang, Jiayi Ma, Chen Chen, Xinwei Jiang, Zheng Wang","year":"2017","venue":"IEEE Trans. Cybern","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jiao et al., 2018] Jiening Jiao, Wei-Shi Zheng, Ancong Wu, Xia- tian Zhu, and Shaogang Gong. Deep low-resolution person re- identification. In AAAI, 2018.","title":"Deep low-resolution person reidentification","authors":"Jiening Jiao, Wei-Shi Zheng, Ancong Wu, Xiatian Zhu, Shaogang Gong","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jing et al., 2015] Xiao-Yuan Jing, Xiaoke Zhu, Fei Wu, Xinge You, Qinglong Liu, Dong Yue, Ruimin Hu, and Baowen Xu. Super-resolution person re-identification with semi-coupled low- rank discriminant dictionary learning. In CVPR, 2015.","title":"Super-resolution person re-identification with semi-coupled lowrank discriminant dictionary learning","authors":"Xiao-Yuan Jing, Xiaoke Zhu, Fei Wu, Xinge You, Qinglong Liu, Dong Yue, Ruimin Hu, Baowen Xu","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Johnson et al., 2016] Justin Johnson, Alexandre Alahi, and Li Fei- Fei. Perceptual losses for real-time style transfer and super- resolution. In ECCV, 2016.","title":"Perceptual losses for real-time style transfer and superresolution","authors":"Justin Johnson, Alexandre Alahi, Li Fei-Fei","year":"2016","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ledig et al., 2016] Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, An- drew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-realistic single image super-resolution using a generative adversarial network. arXiv preprint arXiv:1609.04802, 2016.","title":"Photo-realistic single image super-resolution using a generative adversarial network","authors":"Ledig","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Li et al., 2015] Xiang Li, Wei-Shi Zheng, Xiaojuan Wang, Tao Xi- ang, and Shaogang Gong. Multi-scale learning for low-resolution person re-identification. In ICCV, 2015.","title":"Multi-scale learning for low-resolution person re-identification","authors":"Xiang Li, Wei-Shi Zheng, Xiaojuan Wang, Tao Xiang, Shaogang Gong","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liao et al., 2015] Shengcai Liao, Yang Hu, Xiangyu Zhu, and Stan Z Li. Person re-identification by local maximal occurrence representation and metric learning. In CVPR, 2015.","title":"Person re-identification by local maximal occurrence representation and metric learning","authors":"Shengcai Liao, Yang Hu, Xiangyu Zhu, Stan Z Li","year":"2015","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Roth et al., 2014] Peter M Roth, Martin Hirzer, Martin Köstinger, Csaba Beleznai, and Horst Bischof. Mahalanobis distance learn- ing for person re-identification. In Person Re-Identification. 2014.","title":"Mahalanobis distance learning for person re-identification","authors":"Roth","year":"2014","venue":"Person Re-Identification","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Russakovsky et al., 2015] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Im- agenet large scale visual recognition challenge. Int. J. Comput. Vision, 2015.","title":"Imagenet large scale visual recognition challenge","authors":"Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein","year":"2015","venue":"Int. J. Comput. Vision","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Shi et al., 2016] Wenzhe Shi, Jose Caballero, Ferenc Huszár, Jo- hannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang. Real-time single image and video super- resolution using an efficient sub-pixel convolutional neural net- work. In CVPR, 2016.","title":"Real-time single image and video superresolution using an efficient sub-pixel convolutional neural network","authors":"Shi","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Simonyan and Zisserman, 2014] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale im- age recognition. arXiv preprint arXiv:1409.1556, 2014.","title":"Very deep convolutional networks for large-scale image recognition","authors":"Karen Simonyan, Andrew Zisserman","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang and Bovik, 2009] Zhou Wang and Alan C Bovik. Mean squared error: Love it or leave it? a new look at signal fidelity measures. IEEE Signal Proc. Mag., 2009.","title":"Mean squared error: Love it or leave it? a new look at signal fidelity measures","authors":"Zhou Wang, Alan C Bovik","year":"2009","venue":"IEEE Signal Proc. Mag","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2007] Xiaogang Wang, Gianfranco Doretto, Thomas Sebastian, Jens Rittscher, and Peter Tu. Shape and appearance context modeling. In ICCV, 2007.","title":"Shape and appearance context modeling","authors":"Xiaogang Wang, Gianfranco Doretto, Thomas Sebastian, Jens Rittscher, Peter Tu","year":"2007","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2016a] Zheng Wang, Ruimin Hu, Chao Liang, Yi Yu, Junjun Jiang, Mang Ye, Jun Chen, and Qingming Leng. Zero- shot person re-identification via cross-view consistency. IEEE Trans. Multimedia, 2016.","title":"Zeroshot person re-identification via cross-view consistency","authors":"Wang","year":"2016","venue":"IEEE Trans. Multimedia","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2016b] Zheng Wang, Ruimin Hu, Yi Yu, Junjun Jiang, Chao Liang, and Jinqiao Wang. Scale-adaptive low-resolution person re-identification via learning a discriminating surface. In IJCAI, 2016.","title":"Scale-adaptive low-resolution person re-identification via learning a discriminating surface","authors":"Wang","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/379"},{"position":24,"raw":"[Wang et al., 2017a] Zheng Wang, Ruimin Hu, Chen Chen, Yi Yu, Junjun Jiang, Chao Liang, and Shin'ichi Satoh. Person reidenti- fication via discrepancy matrix and matrix metric. IEEE Trans. Cybern., 2017.","title":"Person reidentification via discrepancy matrix and matrix metric","authors":"Wang","year":"2017","venue":"IEEE Trans. Cybern","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2017b] Zheng Wang, Ruimin Hu, Yi Yu, Junjun Jiang, Jiayi Ma, and Shin'ichi Satoh. Statistical inference of gaussian- laplace distribution for person verification. In ACM Multimedia, 2017.","title":"Statistical inference of gaussianlaplace distribution for person verification","authors":"Wang","year":"2017","venue":"ACM Multimedia","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Ye et al., 2016] Mang Ye, Chao Liang, Yi Yu, Zheng Wang, Qing- ming Leng, Chunxia Xiao, Jun Chen, and Ruimin Hu. Person reidentification via ranking aggregation of similarity pulling and dissimilarity pushing. IEEE Trans. Multimedia, 2016.","title":"Person reidentification via ranking aggregation of similarity pulling and dissimilarity pushing","authors":"Mang Ye, Chao Liang, Yi Yu, Zheng Wang, Qingming Leng, Chunxia Xiao, Jun Chen, Ruimin Hu","year":"2016","venue":"IEEE Trans. Multimedia","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Ye et al., 2017] Mang Ye, Andy J Ma, Liang Zheng, Jiawei Li, and Pong C Yuen. Dynamic label graph matching for unsupervised video re-identification. In ICCV, 2017.","title":"Dynamic label graph matching for unsupervised video re-identification","authors":"Mang Ye, Andy J Ma, Liang Zheng, Jiawei Li, Pong C Yuen","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yu et al., 2017] Hong-Xing Yu, Ancong Wu, and Wei-Shi Zheng. Cross-view asymmetric metric learning for unsupervised person re-identification. In ICCV, 2017.","title":"Cross-view asymmetric metric learning for unsupervised person re-identification","authors":"Yu","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Zhang et al., 2016] Yaqing Zhang, Xi Li, Liming Zhao, and Zhongfei Zhang. Semantics-aware deep correspondence struc- ture learning for robust person re-identification. In IJCAI, 2016.","title":"Semantics-aware deep correspondence structure learning for robust person re-identification","authors":"Yaqing Zhang, Xi Li, Liming Zhao, Zhongfei Zhang","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/500"},{"position":30,"raw":"[Zhao et al., 2017] Liming Zhao, Xi Li, Yueting Zhuang, and Jing- dong Wang. Deeply-learned part-aligned representations for per- son re-identification. In ICCV, 2017.","title":"Deeply-learned part-aligned representations for person re-identification","authors":"Liming Zhao, Xi Li, Yueting Zhuang, Jingdong Wang","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Zheng et al., 2015] Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, and Qi Tian. Scalable person re- identification: A benchmark. In ICCV, 2015.","title":"Scalable person reidentification: A benchmark","authors":"Liang Zheng, Liyue Shen, Lu Tian, Shengjin Wang, Jingdong Wang, Qi Tian","year":"2015","venue":"ICCV","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}