An algorithm for moving target detection in IR image based on grayscale distribution and kernel function
来源期刊：中南大学学报(英文版)2014年第11期
论文作者：WANG Lu-ping(王鲁平) ZHANG Lu-ping(张路平) ZHAO Ming(赵明) LI Biao(李飚)
文章页码：4270 - 4278
Key words：moving target detection; gray-weighted kernel function; dynamic background
Abstract: A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection (MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel (GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.
WANG Lu-ping(王鲁平), ZHANG Lu-ping(张路平), ZHAO Ming(赵明), LI Biao(李飚)
(Science and Technology on Automatic Target Recognition Laboratory,
National University of Defense Technology, Changsha 410073, China)
Abstract:A fast algorithm based on the grayscale distribution of infrared target and the weighted kernel function was proposed for the moving target detection (MTD) in dynamic scene of image series. This algorithm is used to deal with issues like the large computational complexity, the fluctuation of grayscale, and the noise in infrared images. Four characteristic points were selected by analyzing the grayscale distribution in infrared image, of which the series was quickly matched with an affine transformation model. The image was then divided into 32×32 squares and the gray-weighted kernel (GWK) for each square was calculated. At last, the MTD was carried out according to the variation of the four GWKs. The results indicate that the MTD can be achieved in real time using the algorithm with the fluctuations of grayscale and noise can be effectively suppressed. The detection probability is greater than 90% with the false alarm rate lower than 5% when the calculation time is less than 40 ms.
Key words:moving target detection; gray-weighted kernel function; dynamic background
