AN APPROACH FOR IDENTIFYING SOCIAL RELATIONSHIPS BASED ON VIDEOIMAGES ANALYSIS - Problems of Information Technology

AN APPROACH FOR IDENTIFYING SOCIAL RELATIONSHIPS BASED ON VIDEOIMAGES ANALYSIS - Problems of Information Technology

AN APPROACH FOR IDENTIFYING SOCIAL RELATIONSHIPS BASED ON VIDEOIMAGES ANALYSIS - Problems of Information Technology

AN APPROACH FOR IDENTIFYING SOCIAL RELATIONSHIPS BASED ON VIDEOIMAGES ANALYSIS - Problems of Information Technology

AN APPROACH FOR IDENTIFYING SOCIAL RELATIONSHIPS BASED ON VIDEOIMAGES ANALYSIS - Problems of Information Technology
AN APPROACH FOR IDENTIFYING SOCIAL RELATIONSHIPS BASED ON VIDEOIMAGES ANALYSIS - Problems of Information Technology
AZERBAIJAN NATIONAL ACADEMY OF SCIENCES

№2, 2021

AN APPROACH FOR IDENTIFYING SOCIAL RELATIONSHIPS BASED ON VIDEOIMAGES ANALYSIS

Alguliyev Rasim M., Aliguliyev Ramiz M., Alakbarova Irada Y.

Identification and analysis of social relations in society are important issues for the effective management of e-government, to ensure socio-economic development and stability in the country. The analysis of social relations allows to more clearly see the processes taking place in society and existing social problems. The study of some existing methods and approaches for identifying people and objects, for identifying events and social relations as a result of intelligent video analysis is the basis of the object of research. Determining social relationships and predicting anomalous events by observing the behavior of citizens in public places is a very complex process. The article examines some existing approaches for analyzing objects and an event using videoimages obtained using video surveillance systems, proposes a new approach for identifying social relations based on intelligent video analysis, and also develops a general architectural scheme of an intelligent video surveillance system. A step-by-step solution is proposed for analyzing videoimages obtained using video surveillance cameras. The research results can be used to better manage e-government, protect the safety of citizens, and in many other areas (pp.3-15).

Keywords: video surveillance system, videoimages, big data, social relations, video analytics, deep learning.
DOI : 10.25045/jpit.v12.i2.01
References
  • Tay L., Jebb A.T., Woo S.E. Video capture of human behaviors: toward a Big Data approach // Current Opinion in Behavioral Sciences, 2017, vol.18, pp.17–22.
  • Lara O.D., Labrador M.A., A survey on human activity recognition using wearable sensors // IEEE Communications Surveys & Tutorials, 2013, vol.15, no.3, pp.1192–1209.
  • Anwar F., Petrounias I., Morris T., Kodogiannis V. Mining anomalous events against frequent sequences in surveillance videos from commercial environments // Expert Systems with Applications, 2012, vol.39, no.4, pp.4511–4531.
  • Wenqing W. Intelligent Video Surveillance Technology in Intelligent Transportation // Advances in Intelligent Transportation, 2020, https://www.hindawi.com/journals/jat/ 2020/8891449/
  • Babaguchi N., Cavallaro A., Chellappa R., Dufaux F., Wang L. Special issue on intelligent video surveillance for public security and personal privacy // IEEE Transactions on Information Forensics & Security, 2017, vol.16, no.1, pp.8–15.
  • Wang T., Qiao M., Chen Y., Chen J., Snoussi H. Video feature descriptor combining motion and appearance cues with length-invariant characteristics // Optik, 2018, vol.157, pp.1143–1154.
  • Bouchard B., Giroux S., Bouzouane A. A smart home agent for plan recognition of cognitively-impaired patients // Journal of Computers, 2006, vol.1, no.5, pp.53–62.
  • Chen L., Nugent C., Mulvenna M., Finlay D., Hong X., Poland M. A logical framework for behaviour reasoning and assistance in a smart home // International Journal of Assistive Robotics and Mechatronics, 2008, vol.9, no.4, pp.20–34.
  • Francois A.R.J., Nevatia R., Hobbs J., Bolles R.C., Smith J.R. VERL: an ontology framework for representing and annotating video events // IEEE Multimedia, 2005, vol.12, pp.76–86.
  • Haibin Yu., Wenyan J., Zhen Li, Feixiang G., Ding Y., Hong Z., Mingui S. A multisource fusion framework driven by user-defined knowledge for egocentric activity recognition // EURASIP Journal on Advances in Signal Processing, 2019, vol.1, no.14, pp.1–23.
  • Chaudhary S., Khan M.A., Bhatnagar C. Multiple anomalous activity detection in videos // Procedia Computer Science, 2018, no.125, pp.336–345.
  • Ko K.E., Sim K.B. Deep convolutional framework for abnormal behavior detection in a smart surveillance system // Engineering Applications of Artificial Intelligence, 2018, vol.67, pp.226–234.
  • Anomaly Detection with Autoencoders Made Easy, 2009, https://towardsdatascience.com/anomaly-detection-with-autoencoder-b4cdce4866a6
  • Pennisi A., Bloisi D.D., Iocchi L. Online real-time crowd behavior detection in video sequences // Computer Vision and Image Understanding, 2016, 144, pp.166–176.
  • Sreenu G., Saleem M., Durai A. Intelligent video surveillance: a review through deep learning techniques for crowd analysis // Journal of Big Data, 2019, vol.6, no.48, pp.1–27.
  • Wang X., Loy C.C. Chapter 10 - Deep learning for scene-independent crowd analysis // Group and Crowd Behavior for Computer Vision, 2017, pp.209–252.
  • Christian S., Toshev A., Erhan D. Deep Neural Networks for Object Detection // In Advances in Neural Information Processing Systems, 2013, pp.2553–2561.
  • Pathak A.R, Pandey M, Rautaray S. Application of deep learning for object detection // Procedia Computer Science, 2018, vol.132, pp.1706–1716.
  • Couper M. Is the sky falling? New technology, changing media, and the future of surveys // Survey Research Methods, 2013, vol.7, no.3, pp.145–156.
  • Suthaharan S. Big Data Classification: Problems and Challenges in Network Intrusion Prediction with Machine Learning // Performance Evaluation Review, 2014, vol.41, no.4, pp.70–73.
  • Aliguliyev R.M., Alakbarova I.Y. Fardi malumatlarin e-idaraetma sisteminin sosial kredit sisteminda tatbiginin perspektivlari hagginda // Informasiya jamiyyati problemlari, 2021, №1, s.67–76.
  • Rodriguez M., Sivic J., Laptev I. Chapter 5 - The analysis of high density crowds in videos // Group and crowd behavior for computer vision, 2017, pp.89–113.
  • Friedman M., Last M., Makover Y., Kandel A. Anomaly detection in web documents using crisp and fuzzy-based cosine clustering methodology // Information Sciences, 2007, vol.177, no.5, pp.467–475.
  • Alguliyev R.M., Aliguliyev R.M., Alakbarova I.Y. Extraction of hidden social networks from wiki-environment involved in information conflict // International Journal of Intelligent Systems and Applications, 2016, vol.8, no.2, pp. 20–27.
  • Fioramanti M. Predicting Sovereign Debt Crises Using Artificial Neural Networks: A Comparative Approach // Financial Stability, 2008, vol.4, no.2, pp.149-164.
  • Alp Ö.S., Büyükbebeci E., İşcanog A., Özkurt F.Y., Taylan P., Weber G.W. CMARS and GAM & CQP—modern optimization methods applied to international credit default prediction // Computational and Applied Mathematics, 2011, vol.235, no.16, pp.4639–4651.
  • Schebesch K.B., Stecking R. Support vector machines for classifying and describing credit applicants: detecting typical and critical regions // Operations Research Society, 2005, vol.56, no.9, pp.1082–1088.
  • Voronovskij G.K., Mahotilo K.V., Petrashev S.N., Sergeev S.A. Geneticheskie algoritmy, iskusstvennye nejronnye seti i problemy virtual'noj real'nosti, Har'kov, OSNOVA, 1997, 112s.
  • Chen M.C., Huang S.H. Credit scoring and rejected instances reassigning through evolutionary computation techniques // Expert Systems with Applications, 2003, vol.24, no.4, pp.433–441.
  • Chuang C.L., Lin R.H. Constructing a reassigning credit scoring model // Expert Systems with Applications, 2009, vol.36, no.2, pp.1685–1694.