| Summary: | 碩士 === 國立交通大學 === 多媒體工程研究所 === 100 === In nighttime video surveillance, far objects are often hard to be identified due to poor illumination conditions while near objects may be whitened due to over-exposure. Therefore, we used a multiple intensity IR-illuminator that provides multiple illumination levels periodically as a supportive light source. By using the IR-illuminator, images with different degrees of exposure can be obtained. Accordingly, we can detect far human correctly and display near face clearly in the meantime, which is better than using fixed illumination. We analyze multiple intensity videos and propose three foreground detection methods. The first two methods can extract foreground regions by illumination clustering, which can find the period of illumination variation automatically. The third method, based on a periodic min-max model, has lower computational complexity and real-time performance and does not require illumination clustering. Experimental results show that the first and the third methods can achieve more than 90% of average accuracy in foreground object detection for various test videos.
|
|---|
