Object Motion Direction Detection and Tracking for Automatic Video Surveillance

Автор: Adithya Urs, Nagaraju C.

Журнал: International Journal of Education and Management Engineering @ijeme

Статья в выпуске: 2 vol.11, 2021 года.

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In today’s world having a smart reliable surveillance system is very much in need. In fact in many public places like banks, jewellery stores, malls, schools and colleges it is basic necessary to have a surveillance system (CCTV). Most of today’s implementations are not smart and they record videos during night even when there is no motion. This will lead to unnecessary storage usage and difficult to get the important part of the footage. And also, most of the today’s implementations are stationary, they can’t track the moving object. This report will outline a naive approach to implement a smart video surveillance system using object motion detection and tracking. Here we are using conventional Background subtraction model to detect motion and we estimate the direction of motion of object by comparing the centroid of the moving object in subsequent frames and track the moving object by rotating the camera using servo. Video recording takes place only when there is movement in the frame which helps in storage efficiency. We are also improving the speed of email alert delivery by using multithreading.

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Background subtraction Contours, Convolution, Dilation, Erosion, Gaussian blur, Multithreading

Короткий адрес: https://sciup.org/15017292

IDR: 15017292   |   DOI: 10.5815/ijeme.2021.02.04

Список литературы Object Motion Direction Detection and Tracking for Automatic Video Surveillance

  • Arghava, Keivani Jules-Raymon-Tapamo, Farzad Ghayoor "Motion-based Moving Object Detection and Tracking using Automatic K-means", IEEE African 2017 Proceedings.
  • Shesha shah and P.S Sastry Published “Object tracking using motion direction detection” in Indian Institute of Science Bangalore.
  • M Sahasri and C. Gireesh Published “Object motion Detection and tracking for video Surveillance” International journal of engineering trends and technology (IJETT) April 2017.
  • Srinivasan.K, Porkumaran.K, and G. Sainarayan.2010." Improved Background subtraction Techniques for security in Video Applications”.
  • Kaman Kohli, Jatinder pal Singh and Anshul Kumar “Motion detection algorithm”.
  • Omar Elharrouss, Noor Al-Maadeed, Somaya Al-Maadeed “Motion Detection, Tracking and Classification for Automated Video Surveillance" Department of Computer Science and Engineering, Qatar University, IEEE Conference 2019.
  • MANZANERA, Antoine et RICHEFEU, Julien C. A new motion detection algorithm based on Σ–Δ background estimation. Pattern Recognition Letters, 2007, vol. 28, no 3, p. 320-328.
  • CHENG, Fan-Chieh, HUANG, Shih-Chia, et RUAN, Shanq-Jang. Illumination-sensitive background modeling approach for accurate moving object detection.Broadcasting, IEEE Transactions on, 2011, vol. 57, no 4, p. 794-801.
  • WANG, Yong, LU, Qian, WANG, Dianhong, et al. Compressive background modeling for foreground extraction. Journal of Electrical and Computer Engineering, 2015, vol. 2015, p. 13.
  • LIN, Dazhen, CAO, Donglin, et ZENG, Hualin. Improving motion state change object detection by using block background context. In : Computational Intelligence (UKCI), 2014 14th UK Workshop on. IEEE, 2014. p. 1-6.
  • GHAEMINIA, Mohammad Hossein et SHOKOUHI, Shahryar Baradaran. Adaptive background model for moving objects based on PCA. In : Machine Vision and Image Processing (MVIP), 2010 6th Iranian. IEEE, 2010. p. 1-4.
  • LIAO, Shengcai, ZHAO, Guoying, KELLOKUMPU, Vili, et al. Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes. In : Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010. p. 1301-1306.
  • M. Botha, R. Solms, “Utilizing Neural Networks For Effective Intrusion Detection”, ISSA, 2004.
  • Anup Goyal, Chetan Kumar, “GA-NIDS: A Genetic Algorithm based Network Intrusion Detection System”, 2008.
  • Lee W., Stolfo S., and Mok K., “Adaptive Intrusion Detection: A Data Mining Approach,” Artificial Intelligence Review, 14(6), December 2000, pp. 533-567.
  • Stolfo J., Fan W., Lee W., Prodromidis A., and Chan P.K., “Cost-based modeling and evaluation for data mining with application to fraud and intrusion detection,” DARPA Information Survivability Conference, 2000.
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