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 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.