{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T17:17:41Z","timestamp":1772039861528,"version":"3.50.1"},"reference-count":32,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T00:00:00Z","timestamp":1591142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100007515","name":"National Science Foundation","doi-asserted-by":"publisher","award":["REU-1343123, SCC-1737585, ATD-1737996"],"award-info":[{"award-number":["REU-1343123, SCC-1737585, ATD-1737996"]}],"id":[{"id":"10.13039\/100007515","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Spatial Algorithms Syst."],"published-print":{"date-parts":[[2020,12,31]]},"abstract":"<jats:p>Detecting anomalous activity in human mobility data has a number of applications, including road hazard sensing, telematics-based insurance, and fraud detection in taxi services and ride sharing. In this article, we address two challenges that arise in the study of anomalous human trajectories: (1) a lack of ground truth data on what defines an anomaly and (2) the dependence of existing methods on significant pre-processing and feature engineering. Although generative adversarial networks (GANs) seem like a natural fit for addressing these challenges, we find that existing GAN-based anomaly detection algorithms perform poorly due to their inability to handle multimodal patterns. For this purpose, we introduce an infinite Gaussian mixture model coupled with (bidirectional) GANs\u2014IGMM-GAN\u2014that is able to generate synthetic, yet realistic, human mobility data and simultaneously facilitates multimodal anomaly detection. Through the estimation of a generative probability density on the space of human trajectories, we are able to generate realistic synthetic datasets that can be used to benchmark existing anomaly detection methods. The estimated multimodal density also allows for a natural definition of outlier that we use for detecting anomalous trajectories. We illustrate our methodology and its improvement over existing GAN anomaly detection on several human mobility datasets, along with MNIST.<\/jats:p>","DOI":"10.1145\/3385809","type":"journal-article","created":{"date-parts":[[2020,6,3]],"date-time":"2020-06-03T10:06:08Z","timestamp":1591178768000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":32,"title":["Coupled IGMM-GANs with Applications to Anomaly Detection in Human Mobility Data"],"prefix":"10.1145","volume":"6","author":[{"given":"Daniel","family":"Smolyak","sequence":"first","affiliation":[{"name":"University of Maryland, College Park, MD"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kathryn","family":"Gray","sequence":"additional","affiliation":[{"name":"University of Colorado, Linden St. Tucson, AZ"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarkhan","family":"Badirli","sequence":"additional","affiliation":[{"name":"Indiana University--Purdue University Indianapolis, Indianapolis, IN"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Mohler","sequence":"additional","affiliation":[{"name":"Indiana University--Purdue University Indianapolis, Indianapolis, IN"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2020,6,3]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Breckon","author":"Akcay Samet","year":"2018","unstructured":"Samet Akcay , Amir Atapour-Abarghouei , and Toby P . Breckon . 2018 . GANomaly : Semi-supervised anomaly detection via adversarial training. arXiv:1805.06725. Samet Akcay, Amir Atapour-Abarghouei, and Toby P. Breckon. 2018. GANomaly: Semi-supervised anomaly detection via adversarial training. arXiv:1805.06725."},{"key":"e_1_2_1_2_1","volume-title":"Srivastava","author":"Alzantot Moustafa","year":"2017","unstructured":"Moustafa Alzantot , Supriyo Chakraborty , and Mani B . Srivastava . 2017 . SenseGen : A deep learning architecture for synthetic sensor data generation. arxiv:1701.08886. Moustafa Alzantot, Supriyo Chakraborty, and Mani B. Srivastava. 2017. SenseGen: A deep learning architecture for synthetic sensor data generation. arxiv:1701.08886."},{"key":"e_1_2_1_3_1","unstructured":"Matan Ben-Yosef and Daphna Weinshall. 2018. Gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images. arxiv:1808.10356.  Matan Ben-Yosef and Daphna Weinshall. 2018. Gaussian mixture generative adversarial networks for diverse datasets and the unsupervised clustering of images. arxiv:1808.10356."},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557043"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2238531"},{"key":"e_1_2_1_6_1","unstructured":"Jeff Donahue Philipp Kr\u00e4henb\u00fchl and Trevor Darrell. 2016. Adversarial feature learning. arXiv:1605.09782.  Jeff Donahue Philipp Kr\u00e4henb\u00fchl and Trevor Darrell. 2016. Adversarial feature learning. arXiv:1605.09782."},{"key":"e_1_2_1_7_1","volume-title":"Work","author":"Donovan Brian","year":"2015","unstructured":"Brian Donovan and Daniel B . Work . 2015 . Using coarse GPS data to quantify city-scale transportation system resilience to extreme events. arXiv:1507.06011. Brian Donovan and Daniel B. Work. 2015. Using coarse GPS data to quantify city-scale transportation system resilience to extreme events. arXiv:1507.06011."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2015-0029"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3347146.3359088"},{"key":"e_1_2_1_10_1","unstructured":"Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. 2672--2680.  Ian Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron Courville and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. 2672--2680."},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData.2018.8622424"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"e_1_2_1_13_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba . 2014 . Adam : A method for stochastic optimization. arXiv:1412.6980. Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980."},{"key":"e_1_2_1_14_1","volume-title":"Retrieved","author":"LeCun Yann","year":"2020","unstructured":"Yann LeCun , Corinna Cortes , and Christopher J. C. Burges . 2010. The MNIST Database of Handwritten Digits. 2010 . Retrieved May 11, 2020 from http:\/\/yann.lecun.com\/exdb\/mnist. Yann LeCun, Corinna Cortes, and Christopher J. C. Burges. 2010. The MNIST Database of Handwritten Digits. 2010. Retrieved May 11, 2020 from http:\/\/yann.lecun.com\/exdb\/mnist."},{"key":"e_1_2_1_15_1","article-title":"Visualizing data using t-SNE","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton . 2008 . Visualizing data using t-SNE . Journal of Machine Learning Research 9 ( Nov. 2008), 2579--2605. Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9 (Nov. 2008), 2579--2605.","journal-title":"Journal of Machine Learning Research 9"},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the 2018 International Conference on Learning Representations.","author":"Molano-Mazon Manuel","year":"2018","unstructured":"Manuel Molano-Mazon , Arno Onken , Eugenio Piasini , and Stefano Panzeri . 2018 . Synthesizing realistic neural population activity patterns using generative adversarial networks . In Proceedings of the 2018 International Conference on Learning Representations. Manuel Molano-Mazon, Arno Onken, Eugenio Piasini, and Stefano Panzeri. 2018. Synthesizing realistic neural population activity patterns using generative adversarial networks. In Proceedings of the 2018 International Conference on Learning Representations."},{"key":"e_1_2_1_17_1","unstructured":"Sudipto Mukherjee Himanshu Asnani Eugene Lin and Sreeram Kannan. 2018. ClusterGAN: Latent space clustering in generative adversarial networks. arxiv:1809.03627.  Sudipto Mukherjee Himanshu Asnani Eugene Lin and Sreeram Kannan. 2018. ClusterGAN: Latent space clustering in generative adversarial networks. arxiv:1809.03627."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304299"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2525314.2525343"},{"key":"e_1_2_1_20_1","volume-title":"On detection of emerging anomalous traffic patterns using GPS data. Data 8 Knowledge Engineering 87","author":"Pang Linsey Xiaolin","year":"2013","unstructured":"Linsey Xiaolin Pang , Sanjay Chawla , Wei Liu , and Yu Zheng . 2013. On detection of emerging anomalous traffic patterns using GPS data. Data 8 Knowledge Engineering 87 ( 2013 ), 357--373. Linsey Xiaolin Pang, Sanjay Chawla, Wei Liu, and Yu Zheng. 2013. On detection of emerging anomalous traffic patterns using GPS data. Data 8 Knowledge Engineering 87 (2013), 357--373."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.5555\/1702135.1702171"},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the KDD Workshop on Machine Learning for Large Scale Transportation Systems.","author":"Ramaiah Chetan","year":"2016","unstructured":"Chetan Ramaiah , Allen Tran , Evan Cox , and George Mohler . 2016 . Deep learning for driving detection on mobile phones . In Proceedings of the KDD Workshop on Machine Learning for Large Scale Transportation Systems. Chetan Ramaiah, Allen Tran, Evan Cox, and George Mohler. 2016. Deep learning for driving detection on mobile phones. In Proceedings of the KDD Workshop on Machine Learning for Large Scale Transportation Systems."},{"key":"e_1_2_1_23_1","unstructured":"Carl Edward Rasmussen. 1999. The infinite Gaussian mixture model. In Advances in Neural Information Processing Systems. 554--560.  Carl Edward Rasmussen. 1999. The infinite Gaussian mixture model. In Advances in Neural Information Processing Systems. 554--560."},{"key":"e_1_2_1_24_1","volume-title":"Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks. arXiv preprint arXiv:1712.01636","author":"Salehinejad Hojjat","year":"2017","unstructured":"Hojjat Salehinejad , Shahrokh Valaee , Tim Dowdell , Errol Colak , and Joseph Barfett . 2017. Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks. arXiv preprint arXiv:1712.01636 ( 2017 ). Hojjat Salehinejad, Shahrokh Valaee, Tim Dowdell, Errol Colak, and Joseph Barfett. 2017. Generalization of deep neural networks for chest pathology classification in x-rays using generative adversarial networks. arXiv preprint arXiv:1712.01636 (2017)."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/s150102059"},{"key":"e_1_2_1_27_1","volume-title":"Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar.","author":"Zenati Houssam","year":"2018","unstructured":"Houssam Zenati , Chuan Sheng Foo , Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. 2018 . Efficient GAN-based anomaly detection. arXiv:1802.06222. Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. 2018. Efficient GAN-based anomaly detection. arXiv:1802.06222."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2030112.2030127"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/1409635.1409677"},{"key":"e_1_2_1_30_1","volume-title":"GeoLife: A collaborative social networking service among user, location and trajectory","author":"Zheng Yu","year":"2010","unstructured":"Yu Zheng , Xing Xie , and Wei-Ying Ma. 2010. GeoLife: A collaborative social networking service among user, location and trajectory .IEEE Data(base) Engineering Bulletin 33, 2 ( 2010 ), 32--39. Yu Zheng, Xing Xie, and Wei-Ying Ma. 2010. GeoLife: A collaborative social networking service among user, location and trajectory.IEEE Data(base) Engineering Bulletin 33, 2 (2010), 32--39."},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1526709.1526816"},{"key":"e_1_2_1_32_1","volume-title":"August 29, 2019.","author":"Zhou Fan","year":"2019","unstructured":"Fan Zhou , Ruiyang Yin , Goce Trajcevski , Kunpeng Zhang , Jin Wu , and Ashfaq Khokhar . 2019 . Improving human mobility identification with trajectory augmentation. GeoInformatica. Epub ahead of print . August 29, 2019. Fan Zhou, Ruiyang Yin, Goce Trajcevski, Kunpeng Zhang, Jin Wu, and Ashfaq Khokhar. 2019. Improving human mobility identification with trajectory augmentation. GeoInformatica. Epub ahead of print. August 29, 2019."}],"container-title":["ACM Transactions on Spatial Algorithms and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3385809","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3385809","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:32:50Z","timestamp":1750199570000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3385809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,3]]},"references-count":32,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,12,31]]}},"alternative-id":["10.1145\/3385809"],"URL":"https:\/\/doi.org\/10.1145\/3385809","relation":{},"ISSN":["2374-0353","2374-0361"],"issn-type":[{"value":"2374-0353","type":"print"},{"value":"2374-0361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,3]]},"assertion":[{"value":"2020-01-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-02-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-06-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}