The Application of the Moving Object Detection in HD Video Surveillance System

Abstract:

Today the existing image processing systems widely used standard definition resolution. Which is not enough distinct. High definition (HD) and intelligence gradually become the developing trend of the image acquisition and processing system. Motion detection plays an important role in video surveillance system. The sign distribution features will be covered up by the use of the absolute differential image. In this article, a method to determine the motion direction of moving objects by using the sign distribution features in the differential image of two consecutive frames is proposed. To extract the characteristics of the moving object regions,Other parts as the background image is still. The transmission should been stopped, if there is no moving object. These should save storage space and reduce the demand for network speed. Experimental results show that algorithm of the method is suitable for computer processing.

• Periodical: Applied Mechanics and Materials (Volumes 385-386)
• Pages: 1509-1512
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.385-386.1509
• Online since: August 2013
• Authors: Lian Li, Yong Peng Liu
• Keywords: Adjacent Frame Difference Algorithm, HD, Security Monitoring System

References

[1] S. Lee, A 345mW Heterogeneous Many-Core Processor with an Intelligent Inference Engine for Robust Object Recognition, ISSCC Dig. Tech. Papers (2010) p.332.

DOI: 10.1109/isscc.2010.5433905

[2] J.Y. Kim, A 201. 4GOPS 496mW Real-Time Multi-Object Recognition Processor with Bio-inspired Neural Perception Engine, ISSCC Dig. Tech. Papers (2009) p.150.

DOI: 10.1109/isscc.2009.4977352/mm1

[3] J. Oh, A 57mW Embedded Mixed-Mode Neuro-Fuzzy Accelerator for Intelligent Multi-core Processor, ISSCC Dig. Tech. Papers (2011) p.130.

DOI: 10.1109/isscc.2011.5746250

[4] Ren Guangjie, Zhu Qidong, Cao Ning, Analysis of some key technology for high definition video monitoring, Telecommunications Science, Vol. 2(2011), p.119.

[5] J.K. Kearney, W.B. Thompson and D.L. Boley. Optical flow estimation: an error analysis of gradient-based methods with local optimization. IEEE Transactions on Pat-tern Analysis and Machine Intelligence, Vol. 9(1987) p.229.

DOI: 10.1109/tpami.1987.4767897

[6] H.H. Nagel, W. Enkelmann. An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8(1986) p.565.

DOI: 10.1109/tpami.1986.4767833

[7] J. Weng, N. Ahuja, T.S. Huang. Matching two perspective views. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14 (1992) p.806.

DOI: 10.1109/34.149592

[8] M.K. Teal, T.J. Ellis. Target tracking in open world scenes using motion cues and target dynamics, Fifth International Conference on Image Processing and its Applications, Heriot Watt University, UK, Vol. 8(1995) p.276.

DOI: 10.1049/cp:19950664

[9] A.W. Smith, M.K. Teal. Identification and tracking of maritime objects in near-infrared image sequences for collision avoidance, Seventh International Conference on Image Processing and its Applications, Manchester, UK, Vol. 8(1999) p . 250.

DOI: 10.1049/cp:19990321