{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T07:33:28Z","timestamp":1767339208989,"version":"3.41.0"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T00:00:00Z","timestamp":1597190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, therefore has not been fully utilized. With the fast-growing volume of crowd data, such a bottleneck needs to be addressed. In this paper, we propose a new framework which comprehensively tackles this problem. It centers at an unsupervised method for analysis. The method takes as input raw and noisy data with highly mixed multi-dimensional (space, time and dynamics) information, and automatically structure it by learning the correlations among these dimensions. The dimensions together with their correlations fully describe the scene semantics which consists of recurring activity patterns in a scene, manifested as space flows with temporal and dynamics profiles. The effectiveness and robustness of the analysis have been tested on datasets with great variations in volume, duration, environment and crowd dynamics. Based on the analysis, new methods for data visualization, simulation evaluation and simulation guidance are also proposed. Together, our framework establishes a highly automated pipeline from raw data to crowd analysis, comparison and simulation guidance. Extensive experiments and evaluations have been conducted to show the flexibility, versatility and intuitiveness of our framework.<\/jats:p>","DOI":"10.1145\/3386569.3392407","type":"journal-article","created":{"date-parts":[[2020,8,12]],"date-time":"2020-08-12T11:44:27Z","timestamp":1597232667000},"update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Informative scene decomposition for crowd analysis, comparison and simulation guidance"],"prefix":"10.1145","volume":"39","author":[{"given":"Feixiang","family":"He","sequence":"first","affiliation":[{"name":"University of Leeds, United Kingdom"}]},{"given":"Yuanhang","family":"Xiang","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, China"}]},{"given":"Xi","family":"Zhao","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, China"}]},{"given":"He","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Leeds, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2020,8,12]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.382977"},{"key":"e_1_2_2_2_1","volume-title":"A survey on trajectory clustering analysis. CoRR abs\/1802.06971","author":"Bian Jiang","year":"2018","unstructured":"Jiang Bian, Dayong Tian, Yuanyan Tang, and Dacheng Tao. 2018. A survey on trajectory clustering analysis. CoRR abs\/1802.06971 (2018). arXiv:1802.06971"},{"volume-title":"Pattern Recognition and Machine Learning","author":"Bishop Christopher","key":"e_1_2_2_3_1","unstructured":"Christopher Bishop. 2007. Pattern Recognition and Machine Learning. Springer, New York."},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2016.06.016"},{"volume-title":"Computer Graphics Forum","author":"Charalambous Panayiotis","key":"e_1_2_2_5_1","unstructured":"Panayiotis Charalambous, Ioannis Karamouzas, Stephen J. Guy, and Yiorgos Chrysanthou. 2014. A data-driven framework for visual crowd analysis. In Computer Graphics Forum, Vol. 33. Wiley Online Library, 41--50."},{"key":"e_1_2_2_6_1","volume-title":"Menge: A Modular Framework for Simulating Crowd Movement. Collective Dynamics 1, 0","author":"Curtis Sean","year":"2016","unstructured":"Sean Curtis, Andrew Best, and Dinesh Manocha. 2016. Menge: A Modular Framework for Simulating Crowd Movement. Collective Dynamics 1, 0 (2016)."},{"key":"e_1_2_2_7_1","article-title":"Perceptual Effects of Scene Context and Viewpoint for Virtual Pedestrian Crowds","volume":"8","author":"Ennis Cathy","year":"2011","unstructured":"Cathy Ennis, Christopher Edward Peters, and Carol O'Sullivan. 2011. Perceptual Effects of Scene Context and Viewpoint for Virtual Pedestrian Crowds. ACM Transaction on Applied Perception 8, 2, Article Article 10 (Feb. 2011), 22 pages.","journal-title":"ACM Transaction on Applied Perception"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176342360"},{"volume-title":"Hybrid Long-range Collision Avoidance for Crowd Simulation. In ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games. 29--36","author":"Golas Abhinav","key":"e_1_2_2_9_1","unstructured":"Abhinav Golas, Rahul Narain, Sean Curtis, and Ming C. Lin. 2013. Hybrid Long-range Collision Avoidance for Crowd Simulation. In ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games. 29--36."},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2366145.2366209"},{"key":"e_1_2_2_11_1","volume-title":"Social Force Model for Pedestrian Dynamics. Physical Review E","author":"Helbing Dirk","year":"1995","unstructured":"Dirk Helbing and Pet\u00e9r Moln\u00e1r. 1995. Social Force Model for Pedestrian Dynamics. Physical Review E (1995)."},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/2567709.2502622"},{"key":"e_1_2_2_13_1","volume-title":"Crowd Sculpting: A Space-time Sculpting Method for Populating Virtual Environments. Computer Graphics Forum","author":"Jordao K\u00e9vin","year":"2014","unstructured":"K\u00e9vin Jordao, Julien Pettr\u00e9, Marc Christie, and Marie-Paule Cani. 2014. Crowd Sculpting: A Space-time Sculpting Method for Populating Virtual Environments. Computer Graphics Forum (2014)."},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275079"},{"key":"e_1_2_2_15_1","volume-title":"Rousseeuw","author":"Kauffman Leonard","year":"2005","unstructured":"Leonard Kauffman and Peter J. Rousseeuw. 2005. Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley & Sons."},{"key":"e_1_2_2_16_1","volume-title":"Proceedings of the 2007 ACM SIGGRAPH\/Eurographics symposium on Computer animation. 109--118","author":"Lee Kang Hoon","year":"2007","unstructured":"Kang Hoon Lee, Myung Geol Choi, Qyoun Hong, and Jehee Lee. 2007. Group behavior from video: a data-driven approach to crowd simulation. In Proceedings of the 2007 ACM SIGGRAPH\/Eurographics symposium on Computer animation. 109--118."},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/2318896.2318911"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-10347-6_7"},{"key":"e_1_2_2_19_1","volume-title":"ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding. IEEE Conference on Computer Vision and Pattern Recognition","author":"Liu Ning","year":"2019","unstructured":"Ning Liu, Yongchao Long, Changqing Zou, Qun Niu, Li Pan, and Hefeng Wu. 2019. ADCrowdNet: An Attention-injective Deformable Convolutional Network for Crowd Understanding. IEEE Conference on Computer Vision and Pattern Recognition (2019)."},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13629"},{"key":"e_1_2_2_21_1","unstructured":"Barbara Majecka. 2009. Statistical models of pedestrian behaviour in the Forum. MSc Dissertation. School of Informatics University of Edinburgh Edinburgh."},{"key":"e_1_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206641"},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1618452.1618468"},{"key":"e_1_2_2_24_1","volume-title":"The Infinite Gaussian Mixture Model. In International Conference on Neural Information Processing Systems (NIPS'99)","author":"Rasmussen Carl Edward","year":"1999","unstructured":"Carl Edward Rasmussen. 1999. The Infinite Gaussian Mixture Model. In International Conference on Neural Information Processing Systems (NIPS'99). MIT Press, Cambridge, MA, USA, 554--560."},{"key":"e_1_2_2_25_1","volume-title":"Heter-Sim: Heterogeneous multi-agent systems simulation by interactive data-driven optimization. CoRR abs\/1812.00307","author":"Ren Jiaping","year":"2018","unstructured":"Jiaping Ren, Wei Xiang, Yangxi Xiao, Ruigang Yang, Dinesh Manocha, and Xiaogang Jin. 2018. Heter-Sim: Heterogeneous multi-agent systems simulation by interactive data-driven optimization. CoRR abs\/1812.00307 (2018). arXiv:1812.00307"},{"key":"e_1_2_2_26_1","volume-title":"Group modelling: A unified velocity-based approach. Computer Graphics Forum","author":"Ren Zhiguo","year":"2016","unstructured":"Zhiguo Ren, Panayiotis Charalambous, Julien Bruneau, Qunsheng Peng, and Julien Pettr\u00e9. 2016. Group modelling: A unified velocity-based approach. Computer Graphics Forum (2016)."},{"key":"e_1_2_2_27_1","volume-title":"IEEE Transaction on Image Processing","author":"Sabokrou Mohammad","year":"2017","unstructured":"Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, and Reinhard Klette. 2017. Deep-cascade:cascading 3D deep neural networks for fast anomaly detection and localization in crowded scenes. IEEE Transaction on Image Processing (2017)."},{"key":"e_1_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3185596"},{"key":"e_1_2_2_29_1","unstructured":"Long Sha Patrick Lucey Stephan Zheng Taehwan Kim Yisong Yue and Sridha Sridharan. 2017. Fine-grained retrieval of sports plays using tree-based alignment of trajectories. (2017). arXiv:1710.02255"},{"key":"e_1_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.13333"},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/34.868688"},{"key":"e_1_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000302"},{"key":"e_1_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ROBOT.2008.4543489"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2856400.2856410"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2016.2642963"},{"volume-title":"Globally Continuous and Non-Markovian Crowd Activity Analysis from Videos","author":"Wang He","key":"e_1_2_2_36_1","unstructured":"He Wang and Carol O'Sullivan. 2016. Globally Continuous and Non-Markovian Crowd Activity Analysis from Videos. Springer International Publishing, Cham, 527--544."},{"key":"e_1_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00839"},{"key":"e_1_2_2_38_1","volume-title":"IEEE Conference on Computer Vision and Pattern Recognition. 1--8.","author":"Wang Xiaogang","year":"2008","unstructured":"Xiaogang Wang, Keng Teck Ma, Gee-Wah Ng, and W Eric L Grimson. 2008. Trajectory analysis and semantic region modeling using a nonparametric Bayesian model. In IEEE Conference on Computer Vision and Pattern Recognition. 1--8."},{"key":"e_1_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1111\/cgf.12328"},{"key":"e_1_2_2_40_1","volume-title":"Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction. IEEE Conference on Computer Vision and Pattern Recognition","author":"Xu Yanyu","year":"2018","unstructured":"Yanyu Xu, Zhixin Piao, and Shenghua Gao. 2018. Encoding Crowd Interaction with Deep Neural Network for Pedestrian Trajectory Prediction. IEEE Conference on Computer Vision and Pattern Recognition (2018)."},{"key":"e_1_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298971"},{"key":"e_1_2_2_42_1","volume-title":"International Conference on Neural Information Processing Systems.","author":"Yurochkin Mikhail","year":"2016","unstructured":"Mikhail Yurochkin and XuanLong Nguyen. 2016. Geometric Dirichlet Means Algorithm for topic inference. In International Conference on Neural Information Processing Systems."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3386569.3392407","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3386569.3392407","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T05:37:08Z","timestamp":1750829828000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3386569.3392407"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,12]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2020,8,31]]}},"alternative-id":["10.1145\/3386569.3392407"],"URL":"https:\/\/doi.org\/10.1145\/3386569.3392407","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"type":"print","value":"0730-0301"},{"type":"electronic","value":"1557-7368"}],"subject":[],"published":{"date-parts":[[2020,8,12]]},"assertion":[{"value":"2020-08-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}