Small Moving Target Detection In Infrared Image Sequences Based On Temporal Filtering

Small moving target detection algorithm is one of the most importance key technologies in warning systems and imaging-guidance systems, and also is a significant method to improve the operating range detection probability of the systems. To explore and study the new theory of small target detection, as well as how to test the available theory is an important issue, which has great significance for modern and intending warfare. As a target far away from a detector, the image of the target is small and the information of the spatial is lack, which makes target detecting difficultlly. In this paper, we will investigate the small moving target detection in infrared image sequences. The following works are carried out:1. The characteristic of temporal profile in infrared image pixels are analyzed. Based on the temporal behavior of different types of pixels, the means and the variances of temporal profile are discussed. And of which the differences of clear sky background, cloud clutter, and target are compared. After that, by using the Fourier transform, the spectrum of temporal profile is also analyzed. By setting the different band-pass filter, the temporal profiles of clear background, cloud clutter, and target are filtered, from which we investigate the spectrum characteristic in different frequency bands.2. Several temporal profile based algorithms are proposed. To deal with the drawback of large scale data processing and real-time implementation in temporal filtering, a new background elimination method is presented. Based on this, we proposed to use temporal filtering to remove the impact of the large fluctuation of cloud edges. Detailed analysis is focused on the line of connecting line of the stagnation points based filtering method, the minimum filtering method, median filtering method, mathematical morphology filtering method, average filtering method, linear filtering method and the Savitzky-Golay filtering method. And further, the deviation of the temporal profile and its baseline is analyzed, which lead to a detection criterion. Finally the sequential formulas of the proposed algorithms are also given.3. The combination of spatial and temporal filtering to improve the small moving target detection performance in infrared image sequences is discussed. Several typical spatial filtering algorithms are reviewed. By combining the background prediction based algorithm and temporal filtering based algorithm and considering the continuous of the trajectories of dim point targets a spatial and temporal combined detection algorithm is presented. Based on the analysis of the probable trajectories of moving dim point targets, a group of filter templates are constructed. And the trajectory of dim moving target is obtained by using the constructed template to filter the temporal detection result. Also the target occurrence time in each pixel is extract from the temporal based algorithm to further eliminate interference of background.4. Small moving target accumulated method in image sequences is demonstrated. We analyze the dynamic programming based algorithm for dim signal accumulating in multi-frame image sequences. Considering the advantage and disadvantage of dynamic programming algorithm available, a new small moving target detection algorithm in infrared image sequences is presented to reduce the energy scattering in dynamic programming based algorithm. Based on the property of target motion a Gaussian template is built to model target position in the next frame. Our algorithm uses probability not hard constrain, so it can overcome the randomicity of target motion.