Effective Moving Object Detection Over Variable Bit-Rate Wireless Video Streaming

• Main Authors: Jui-Yu Yen, 嚴瑞友
• Other Authors: Shih-Chia Huang
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/28nn8v

碩士 === 國立臺北科技大學 === 電腦與通訊研究所 === 100 === Motion detection plays an important role in 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 variable bit-rate video streams to match the available network bandwidth. However, effective detection of moving objects in variable 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 variable bit-rate video streams. The proposed method is composed of two important modules: a various background generation module and a moving object extraction module. The proposed various background generation module is employed in order to generate the adaptive background model which can express properties of variable bit-rate video streams. After an adaptive background model is generated by using the various background generation module, the proposed moving object extraction module is employed to detect moving objects effectively from both low-quality and high-quality video streams. Lastly, the binary motion detection mask can be generated as the detection result by the output value of the counter-propagation network. In this paper, 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 83.34% and 89.71%, respectively.