Real Time System for Human Identification and Tracking from Surveillance Videos

Authors

  • D Vishaka Gayathri
  • Shrutee Shree
  • Taru Jain
  • K Sornalakshmi

DOI:

https://doi.org/10.14419/ijet.v7i3.12.16034

Published:

2018-07-20

Keywords:

Face detection, Face identification, Real time system, Distributed streaming analytics

Abstract

The need for intelligent surveillance systems has raised the concerns of security. A viable system with automated methods for person identification to detect, track and recognize persons in real time is required. The traditional detection techniques have not been able to analyze such a huge amount of live video generated in real-time. So, there is a necessity for live streaming video analytics which includes processing and analyzing large scale visual data such as images or videos to find content that are useful for interpretation. In this work, an automated surveillance system for real-time detection, recognition and tracking of persons in video streams from multiple video inputs is presented. In addition, the current location of an individual can be searched with the tool bar provided. A model is proposed, which uses a messaging queue to receive/transfer video feeds and the frames in the video are analyzed using image processing modules to identify and recognize the person with respect to the training data sets. The main aim of this project is to overcome the challenges faced in integrating the open source tools that build up the system for tagging and searching people.

 

 

References

[1] Dinesh Singh, C. Vishnu and C. Krishna Mohan, “Visual Big Data Analytics For Traffic Monitoring In Smart Cityâ€, 15th IEEE International Conference on Machine Learning and Applications, 2016

[2] Eigenfaces Vs. Fisherfaces: Recognition Using Class Specific Linear Projection

[3] H. Bhaskar, “Integrated Human Target Detection, Identification And Tracking For Surveillance Applications,†IEEE Intl’ Conf. On Intelligent Systems, P.467–475, 2012.

[4] H. Moon, P.J. Phillips, Computational And Performance Aspects Of PCA-Based Face Recognition Algorithms, Perception, Vol. 30, 2001, Pp. 303-321

[5] Honghai Liu, Shengyong Chen, Naoyuki Kubota, “Intelligent Video Systems And Analytics: A Surveyâ€, IEEE Transactions on Industrial Informatics, Volume: 9, Issue: 3, Aug. 2013,

[6] J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, Face Recognition Using LDA-Based Algorithms, IEEE Trans. On Neural Networks, Vol. 14, No. 1, January 2003, Pp. 195-200

[7] J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, Face Recognition Using Kernel Direct Discriminant Analysis Algorithms, IEEE Trans. On Neural Networks, Vol. 14, No. 1, January 2003, Pp. 117-126

[8] K. Jonsson, J. Matas, J. Kittler, Y.P. Li, Learning Support Vectors For Face Verification And Recognition, Proc. Of The IEEE International Conference On Automatic Face And Gesture Recognition, 26-30 March 2000, Grenoble, France, Pp. 208-213

[9] M.S. Bartlett, J.R. Movellan, T.J. Sejnowski, Face Recognition By Independent Component Analysis, IEEE Trans. On Neural Networks, Vol. 13, No. 6, November 2002, Pp. 1450-1464

[10]R. Chellappa, C.L. Wilson, S.Sirohey, “Human And Machine Recognition Of Faces: A Surveyâ€, Proceedings of the IEEE, Volume: 83, Issue: 5, May 1995

[11]S. Biswas, G. Aggarwal, P. Flynn, “Face Recognition In Low-Resolution Videos Using Learning-Based Likelihood Measurement Model,†Intl’ Joint Conf. On Biometrics, P.1–7, 2011

[12] S. Srisuk, M. Petrou, W. Kurutach And A. Kadyrov, Face Authentication Using The Trace Transform, Proceedings Of The IEEE Computer Society Conference On Computer Vision And Pattern Recognition (CVPR'03), 16-22 June 2003, Madison, Wisconsin, USA, Pp. 305-312

[13] Samal, A., & Iyengar, P. A. (1992). Automatic Recognition And Analysis Of Human Faces And Facial Expressions: A Survey. Pattern Recognition, 25(1), 65-77.

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How to Cite

Vishaka Gayathri, D., Shree, S., Jain, T., & Sornalakshmi, K. (2018). Real Time System for Human Identification and Tracking from Surveillance Videos. International Journal of Engineering & Technology, 7(3.12), 244–247. https://doi.org/10.14419/ijet.v7i3.12.16034

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Received 2018-07-22
Accepted 2018-07-22
Published 2018-07-20