Jui-Yu Yen
ABSTRACT
Motion detection plays an important role in the video surveillance system. Video communications over wireless networks can easily suffer from network congestion or unstable bandwidth, especially for embedded application. A rate control scheme produces various bit-rate video streams to match the available network bandwidth. However, effective detection of moving objects in various bit-rate video streams is a very difficult problem. This paper proposes an advanced approach based on the counter-propagation network through artificial neural networks to achieve effective moving object detection in various bit-rate video streams. We compare our method with other state-of-the-art methods. To demonstrate the performance of our proposed method in regard to object extraction, we analyze qualitative and quantitative comparisons in real-world limited bandwidth networks over a wide range of natural video sequences. The overall results show that our proposed method substantially outperforms other state-of-the-art methods by Similarity and F1 accuracy rates of 73.84% and 84.94%, respectively.
- Kim, W., and Jeong, J., 2010. Complexity control strategy for real-time H.264/AVC encoder. IEEE Trans. Consum. Electron. 56 (May 2010), 1137--1143. Google Scholar
Digital Library
- Robert, H. N., 1987. Counterpropagation networks. A, pplied Optics 26 (Dec. 1987), 4979--4984.Google Scholar
- H.264/AVC Reference Software JM {Online}. Available: http://bs.hhi.de/ suehring/tml/Google Scholar
- Manzanera, A., and Richefeu, J. C., 2004. A robust and computationally efficient motion detection algorithm based on £-Δ background estimation. Proc. ICVGIP'04, 46--51.Google Scholar
- Manzanera, A., and Richefeu, J. C., 2007. New Motion Detection Algorithm Based on £-Δ Background Estimation. Pattern Recognit. Lett. (Feb. 2007), 320--328. Google ScholarDigital Library
- Zhou, D., and Zhang, H., 2005. Modified GMM background modeling and optical flow for detection of moving objects. Int. Conf. on Systems, Man, and Cybernetics 3, 2224--2229.Google ScholarCross Ref
- Jodoin, P. M., and Konrad, J., 2007. Statistical Background Subtraction Using Spatial Cues. IEEE Trans. Circuits Syst. Video Technol. 17 (Dec. 2007), 1758--1763. Google ScholarDigital Library
- Ha, J. E., and Lee, W. H., 2010. Foreground objects detection using multiple difference images. Optical Engineering 49 (Apr. 2010), 047--201.Google Scholar
- Huang, S. C., 2011. An Advanced Motion Detection Algorithm With Video Quality Analysis for Video Surveillance Systems. IEEE Trans. Circuits Syst. Video Technol. 21 (Jan. 2011), 1--14. Google ScholarDigital Library
Index Terms
- Enhanced extraction of moving objects in variable bit-rate video streams
Recommendations
PNN Based Motion Detection with Adaptive Learning Rate
CIS '09: Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 01This paper proposed a new motion detection algorithm based on neural network (NN). Video background was modeled by combing probabilistic neural network (PNN) and winner take all (WTA) network, which is called adaptive background PNN (ABPNN). Every pixel ...
Probabilistic neural networks based moving vehicles extraction algorithm for intelligent traffic surveillance systems
Automated vehicle detection plays an essential role in the traffic video surveillance system. Video communication of these traffic cameras over real-world limited bandwidth networks can frequently suffer network congestion or unstable bandwidth, ...
Counter-propagation artificial neural network-based motion detection algorithm for static-camera surveillance scenarios
Motion detection plays an important role in most static-camera video surveillance systems, yet video communications over wireless networks can easily suffer from network congestion or unstable bandwidth, especially for embedded applications. A rate ...
Comments