Counter-propagation artificial neural network-based motion detection algorithm for static-camera surveillance scenarios

• Published in: Neurocomputing (Amsterdam) Vol. 273; pp. 481 - 493
• Main Authors: Chen, Bo-Hao, Huang, Shih-Chia, Yen, Jui-Yu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 17.01.2018
• Subjects: Motion detection; Neural network; Video surveillance; Video surveillance; Motion detection; Neural network
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2017.08.002

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 control scheme produces variable bit rate video streams to match the available network bandwidth. However, effectively detecting moving objects in a variable bit rate video stream is a considerable challenge. This paper proposes an advanced approach based on a counter-propagation artificial neural network to achieve effective moving-object detection in such conditions. Qualitative and quantitative tests over real-world limited bandwidth networks show that the proposed method substantially outperforms other state-of-the-art methods.