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In order to solve the problem of detecting small dim targets in infrared images with the complicated air background,an improved algorithm based on image complexity and orientation gradient is put forward.The information entropy reflects the complexity of the image.The weighted information entropy with the variance and the change rate of local pixels suppresses both the inside and the edge of clouds.Since the image of the complexity is obtained,targets can be extracted by the means of multi-degree and multi-orientation gradient under the circumstance that the complexity of the background is higher than that of the targets.Experiments indicate that the algorithm can detect small dim targets effectively with complicated cloud background.

Aiming at the problem that traditional Meanshift algorithm can not guarantee tracking accuracy in certain interference or occlusion case and background pixel in object model will induce localization error,a improved targets tracking algorithm based on Meanshift algorithm was proposed.Firstly,the object model is improved by introducing weights which is decided by divisional degree between object and background,and then the weight was used in object model to reduce the localization error of object tracking.Secondly,the target in the tracking window is divided into a number of fragments,the improved Mean Shift algorithm is used in every fragments separately.Finally,the match degree of each fragment is computed and the fragment with the highest match degree is involved to achieve the whole target's coordinates and to avoid the influence of occluded fragments on the object location.Experimental results show that the new method can effectively and accurately track moving target in the cluttering background.