Smart Stadium for Smarter Living: Enriching the Fan Experience

Rapid urbanization has led to more people residing in cities than ever before, and projections estimate that 64% of the global population will be urban by 2050. Cities are beginning to explore Smart City initiatives to reduce expenses and complexities while increasing efficiency and quality of life for its citizens. To achieve this goal, advances in technology and policies are needed together with rethinking traditional solutions to transportation, safety, sustainability, among other priority areas. We propose the use of a Smart Stadium as a 'living laboratory' to identify, deploy and test Internet of Things technologies and Smart City solutions in an environment small enough to practically trial but large enough to evaluate effectiveness and scalability. The Smart Stadium for Smarter Living initiative brings together Arizona State University, Dublin City University, Intel Corporation, Gaelic Athletic Association, Sun Devil Stadium and Croke Park to explore smart environment solutions.

[1]  Tal Hassner,et al.  Violent flows: Real-time detection of violent crowd behavior , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[2]  Andrew Zisserman,et al.  Learning To Count Objects in Images , 2010, NIPS.

[3]  P. KaewTrakulPong,et al.  An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection , 2002 .

[4]  Sridha Sridharan,et al.  Crowd Counting Using Multiple Local Features , 2009, 2009 Digital Image Computing: Techniques and Applications.

[5]  Pekka Ruusuvuori,et al.  Computational Framework for Simulating Fluorescence Microscope Images With Cell Populations , 2007, IEEE Transactions on Medical Imaging.

[6]  Serge J. Belongie,et al.  Counting Crowded Moving Objects , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[7]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[8]  Noel E. O'Connor,et al.  Holistic features for real-time crowd behaviour anomaly detection , 2016, 2016 IEEE International Conference on Image Processing (ICIP).

[9]  Pietro Perona,et al.  Pedestrian detection: A benchmark , 2009, CVPR.

[10]  Shaogang Gong,et al.  Cumulative Attribute Space for Age and Crowd Density Estimation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Xiaogang Wang,et al.  Deeply learned attributes for crowded scene understanding , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Shaogang Gong,et al.  Feature Mining for Localised Crowd Counting , 2012, BMVC.

[13]  Hai Tao,et al.  Counting Pedestrians in Crowds Using Viewpoint Invariant Training , 2005, BMVC.

[14]  Narendra Ahuja,et al.  Extracting Texels in 2.1D Natural Textures , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[15]  Sheng-Fuu Lin,et al.  Estimation of number of people in crowded scenes using perspective transformation , 2001, IEEE Trans. Syst. Man Cybern. Part A.

[16]  Nuno Vasconcelos,et al.  Privacy preserving crowd monitoring: Counting people without people models or tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Hai Tao,et al.  A Viewpoint Invariant Approach for Crowd Counting , 2006, 18th International Conference on Pattern Recognition (ICPR'06).