WEI Xinlei, DU Junping, LIANG Meiyu, XUE Zhe. Crowd Density Field Estimation Based on Crowd Dynamics Theory and Social Force Model[J]. Chinese Journal of Electronics, 2019, 28(3): 521-528. doi: 10.1049/cje.2019.03.021
Citation: WEI Xinlei, DU Junping, LIANG Meiyu, XUE Zhe. Crowd Density Field Estimation Based on Crowd Dynamics Theory and Social Force Model[J]. Chinese Journal of Electronics, 2019, 28(3): 521-528. doi: 10.1049/cje.2019.03.021

Crowd Density Field Estimation Based on Crowd Dynamics Theory and Social Force Model

doi: 10.1049/cje.2019.03.021
Funds:  This work is supported by the National Natural Science Foundation of China(No.61532006, No.61772083, No.61877006, No.61802028).
More Information
  • Corresponding author: DU Junping (corresponding author) was born in 1963. She is now a full professor and Ph.D. supervisor in School of Computer Science and Technology, Beijing University of Posts and Telecommunications. Her research interests include artificial intelligence, image processing and pattern recognition. (Email:junpingdu@126.com)
  • Received Date: 2017-05-18
  • Publish Date: 2019-05-10
  • The local crowd density and the crowd distribution estimation tasks are useful but challenging. There are two main problems which limit the performance of existing algorithms. The first one is that there are not enough labeled training samples to build the highperformance estimation model. Another one is that existing methods lack the supports of physical theories of the crowd. To remedy them, a novel crowd density field model is proposed, which is deduced by jointing crowd dynamics theory and social force model. A crowd counting method based on the proposed crowd density field model is introduced to measure the proposed crowd density field model. Extensive experiments confirm the effectiveness of the proposed plan.
  • loading
  • Guo J., Gong X., Liang J., et al., "An optimized hybrid unicast/multicast adaptive video streaming scheme over MBMS-enabled wireless networks", IEEE Transactions on Broadcasting, Vol.64, No4, pp.791-802,2018.
    Xinlei Wei, Junping Du, Meiyu Liang, et al., "Boosting deep attribute learning via support vector regression for fast moving crowd counting", Pattern Recognition Letters, Vol.119,No.1, pp.12-23, 2019.
    Li Yuhong, Xiaofan Zhang and Deming Chen, "CSRNet:Dilated convolutional neural networks for understanding the highly congested scenes", 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.1091-1100, 2018.
    Jiang Liu, Chenqiang Gao, Deyu Meng, et al., "DecideNet:Counting varying density crowds through attention guided detection and density estimation", 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp.5197-5206, 2018.
    Zhu S., Du J. and Ren N., "A novel simple visual tracking algorithm based on hashing and deep learning", Chinese Journal of Electronics, Vol.26, No.5, pp.1073-1078, 2017.
    Ji Qing-ge, He Hao and Wang Fu-chuan, "Social force model for crowd simulation using density field", Computer Science, Vol.42, No.6,pp.12-17,2015.
    Lin Huang, Jianhua Gong, Wenhang Li, et al., "Social force model-based group behavior simulation in virtual geographic environments", International Journal of Geo-Information, Vol.7, No.3, pp.79, 2018.
    Bellomo Nicola and DOGBé CHRISTIAN, "On the modelling crowd dynamics from scaling to hyperbolic macroscopic models", Math. Models Methods Appl. Sci., Vol.18, No.2008, pp.1317-1345, 2008.
    N. Bellomo, S. Berrone, L. Gibelli, et al., "Macroscopic first order models of multicomponent human crowds with behavioral dynamics", Advances in Computational FluidStructure Interaction and Flow Simulation, Springer International Publishing Switzerland, pp.295-306, 2016.
    A. S. Rao, J. Gubbi, S. Marusic, et al., "Estimation of crowd density by clustering motion cues", The Visual Computer, Vol.31, No.11, pp.1533-1552, 2015.
    Baoxi Liu, Hong Liu, Hao Zhang, et al., "A social force evacuation model driven by video data", Simulation Modelling Practice and Theory, Vol.84, pp.190-203, 2018.
    Zhe Xue, Guorong Li and Qingming Huang, "Joint multiview representation learning and image tagging", Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, Phoenix, Arizona, USA, pp.1366-1372, 2016.
    Liang Meiyu, Du Junping and Zhou Yipeng, "Cross-media hot topic auto-tracking model based on semantics and temporal context", Chinese Journal of Electronics, Vol.24, No.3, pp. 529-534, 2015.
    Liang Meiyu, Du Junping and Li, Linghui, "Video superresolution reconstruction based on correlation learning and spatio-temporal nonlocal similarity", Multimedia Tools and Applications, Vol.75, No.17, pp.10241-10269, 2016.
    Geng Yue, Du Junping and Liang Meiyu, "Abnormal event detection in tourism video based on salient spatio-temporal features and sparse combination learning", World Wide Web Journal, DOI: 10.1007/s11280-018-0603-0.
    Sindagi, V. A. and V. M. Patel, "Generating high-quality crowd density maps using contextual pyramid CNNs", 2017 IEEE International Conference on Computer Vision, Venice, Italy, pp.1879-1888, 2017.
    Muhammad Shahid Saleem, Muhammad Jaleed Khan, Khurram Khurshid, et al., "Crowd density estimation in still images using multiple local features and boosting regression ensemble", Neural Computing and Applications, DOI: 10.1007/s00521-019-04021-2.
    Helbing, Dirk, Farkas, et al., "Simulating dynamical features of escape panic", Nature, Vol. 407, No.6803, pp.487-490, 2000.
    Yanhao Zhang, Lei Qin, Rongrong Ji, et al., "Social attribute-aware force model:Exploiting richness of interaction for abnormal crowd detection", IEEE Transactions on Circuits and Systems for Video Technology, Vol.25,No.7 pp.1231-1245, 2015.
    Brox, Thomas and Jitendra Malik, "Large displacement optical flow:Descriptor matching in variational motion estimation", IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.33, No.3, pp.500-513, 2011.
    Xia Wei, Zhang Junping and Kruger Uwe, "Semi-supervised pedestrian counting with temporal and spatial consistencies", IEEE Transactions on Intelligent Transportation Systems, Vol.16, No.4, pp.1705-1714, 2015.
    A. B. Chan, Z. S. J. Liang and N. Vasconcelos, "Privacy preserving crowd monitoring:Counting people without people models or tracking", 2008 IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, pp.1-7, 2008.
    Chan A. B. and N. Vasconcelos, "Counting People with LowLevel Features and Bayesian regression", IEEE Transactions on Image Processing, Vol.21, No.4, pp.20160-2177, 2012.
    Venkatesh Bala Subburaman, Adrien Descamps, Cyril Carincotte, et al., "Counting people in the crowd using a generic head detector", IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance, Beijing, China, pp.470-475, 2012.
    Jian Yao and Jean-Marc Odobez, "Multi-layer background subtraction based on color and texture", IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, pp.1-8, 2007.
    Olivier Barnich and Marc Van Droogenbroeck, "ViBe:A universal background subtraction algorithm for video sequences", IEEE Transactions on Image Processing, Vol.20, No.6, pp.1709-1724, 2011.
    Chen Change Loy, Shaogang Gong and Tao Xiang, "From semi-supervised to transfer counting of crowds", International Conference on Computer Vision,Sydney, NSW, Australia, pp.2256-2263, 2013.
    Almer A., Perko R., Schrom-Feiertag H., et al., "Critical situation monitoring at large scale events from airborne video based crowd dynamics analysis", Geospatial Data in a Changing World, Springer International Publishing, Switzerland, pp.351-368, 2016.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (179) PDF downloads(183) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return