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.