Abstract
As the core technology of intelligent monitoring system, the moving target detection method plays an important role in the intelligent monitoring system, but the current moving target detection method still has some shortages such as lower anti-jamming performance on the environment, low judge accuracy and so on. In this paper, the improved background subtraction based on mixed Gaussian probability background model is used, local optical flow algorithm is introduced to achieve the unconstrained stable initialization of background model, the added items which are sensitive to global illumination conditions are adopted to assist the adaptive updating of model parameters, eventually the improved algorithm framework are formed and combined with processing method after testing to extract the information of effective movement area and realize the judgment of break in of foreign matters. Finally, the overall realization of intelligent monitoring system is expounded theoretically in the paper, based on the improved moving target detection method, the factors of characteristics of moving targets which affect the detecting precision are analyzed systematically, so it shows that this moving target detection method can improve the detecting precision of intelligent monitoring system and has good real time, accuracy and reliability.
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References
Verri, A., Uras, S., DeMicheli, E.: Motion Segmentation from optical flow. In: Proc. the 5th Alvey Vision Conference, Brighton, UK, pp. 209–214 (1989)
Barron, J., Fleet, D., Beauchemin, S.: Performance of optical flow techniques. International Journal of Computer Vision, 42–77 (December 1994)
Lipton, A., Fuyiyoshi, H., Patil, R.: Moving target classification and tracking from real-time video. In: Proc. IEEE Workshop on Applications of Computer Vision, Princeton, NJ (August 1998)
Haritaoglu, I., Harwood, D., Davis, L.S.: Real-time surveillance of people and their activities. Proc. IEEE Transactions on Pattern Analysis and Machine Intelligence, 809–830 (August 2000)
Gutchess, D., Trajkonnic, M., Cohen-Solal, E., Lyons, D., Jain, A.K.: A background model initialization algorithm for video surveillance. In: Proc. of the 8th IEEE International Conference on Computer Vision, Vancouver, pp. 733–740 (2001)
Chen, Qiuqi, Gonzalez: Digital Image Processing, 2nd edn. Publishing House of Electronics Industry, Beijing (2007)
Cai, H.: Algorithm and the application on video moving targets detection. Master Dissertation, Zhejiang university, Hangzhou (2008)
Dan, Y.: Video moving targets detection under complicated conditions. Ph.D, Thesis, National university of defense technology, Changsha (2006)
Fu, C.: The image sequence moving targets detection and technology research based on mixed Gaussian model. Master Dissertation, Wuhan university of technology, Wuhan (2009)
Du, J.: Moving object detection and tracking algorithms in intelligent video monitoring. Master Dissertation, Southwest Jiaotong university, Chengdu (2009)
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Jia, D., Chen, X. (2012). An Improved Moving Target Detection Method and the Analysis of Influence Factors. In: Tan, Y., Shi, Y., Ji, Z. (eds) Advances in Swarm Intelligence. ICSI 2012. Lecture Notes in Computer Science, vol 7332. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31020-1_38
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DOI: https://doi.org/10.1007/978-3-642-31020-1_38
Publisher Name: Springer, Berlin, Heidelberg
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