INFORMATION CHANGE THE WORLD

International Journal of Information Technology and Computer Science(IJITCS)

ISSN: 2074-9007 (Print), ISSN: 2074-9015 (Online)

Published By: MECS Press

IJITCS Vol.8, No.8, Aug. 2016

IP Camera Based Video Surveillance Using Object's Boundary Specification

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Author(s)

Natalia Chaudhry, Kh. M. Umar Suleman

Index Terms

SURF feature matching;template matching;Kalman filtering;track;IP cameras

Abstract

The ability to detect and track object of interest from sequence of frames is a critical and vital problem of many vision systems developed as yet. This paper presents a smart surveillance system that tracks objects of interest in a sequence of frames in their own defined respective boundaries. The objects of interest are registered or saved within the system. We have proposed a unique tracking algorithm using combination of SURF feature matching, Kalman filtering and template matching approach. Moreover, an efficient technique is proposed that is used to refine registered object image, extract object of interest and remove extraneous image area from it. The system will track registered objects in their respective boundaries using real time video generated through two IP cameras positioned in front of each other.

Cite This Paper

Natalia Chaudhry, Kh. M. Umar Suleman,"IP Camera Based Video Surveillance Using Object's Boundary Specification", International Journal of Information Technology and Computer Science(IJITCS), Vol.8, No.8, pp.13-22, 2016. DOI: 10.5815/ijitcs.2016.08.02

Reference

[1]M. Sivarathinabala and S. Abirami, "An Intelligent Video Surveillance Framework for Remote Monitoring," in International Journal of Engineering Science and Innovative Technology, Vol. 2, 2013. 

[2]S. Huang and J. Hong, "Moving object tracking system based on camshift and Kalman filter," in Consumer Electronics, Communications and Networks, pp. 1423-1426, 2011.

[3]Y.T. Hwang et al., "Feature Points Based Video Object Tracking for Dynamic Scenes and Its FPGA System Prototyping," in Intelligent Information Hiding and Multimedia Signal Processing, pp. 325 - 328, 2014. 

[4]C. Tuscano, et al. "Smart Web Cam Motion Detection Surveillance System," in International Journal of Modern Engineering Research, Vol. 3, pp-1169-1171, 2013. 

[5]S. Chandana, "Real time video surveillance system using motion detection," in India Conference, pp. 1-6, 2011. 

[6]A. Girgensohn, et al. "DOTS: support for effective video surveillance," in Proceedings of the 15th international conference on Multimedia. ACM, pp. 423-432, 2007.

[7]T. Yang, et al. "Robust people detection and tracking in a multi-camera indoor visual surveillance system," in International Conference on Multimedia & Expo, 2007.

[8]K. Xiang, et al. "Intelligent video surveillance for checking attendance of traffic controllers in level crossing," in Journal of Shanghai Jiaotong University, Vol. 19, pp. 41-49, 2014. 

[9]M. Shneier, "Using Pyramids to Define Local Thresholds for Blob Detection," in  Pattern Analysis and Machine  Intelligence, IEEE, Vol. 5, pp. 345 - 349, 1983.

[10]A. Bishnoi, "Noise Removal with Morphological Operations Opening and Closing Using Erosion and Dilation," in International Journal Of Modern Engineering Research, Vol. 4, 2014.

[11]H. Bay,  T. Tuytelaars and  L.V. Gool, "SURF: Speeded up robust features," in  European Conference on  Computer Vision, Vol. 3951, pp. 404-417

[12]Tutorials on Opencv template matching approaches and implementation algorithms working. Available:  http://docs.opencv.org/doc/tutorials/imgproc/histograms/t  emplate_matching/template_matching.html 

[13]C. Suliman, C. Cruceru, and F. Moldoveanu, "Kalman filter based tracking in an video surveillance system," in Advances in Electrical and Computer Engineering, pp. 30-34, 2010.