提出了一种采用小波变换对复杂动态背景变化程度的检测方法。对红外序列图像进行小波变换预处理后,可以提取出若干个特征区域,通过检测这些特征区域的差异来判断背景的变化程度,实现背景抑制。在抑制动态背景的基础之上,运用基于动态先验知识的区域主动轮廓模型的水平集方法来实现红外多目标的数据关联和跟踪,动态先验知识包括形状描述因子、灰度特征和运动特征等。同时,在多目标跟踪中存在目标“合并和分裂”的现象,运用“记忆和填充”方法来实现对多目标的稳定跟踪。通过对实际复杂动态背景条件下的红外序列图像进行多目标跟踪和检测实验,验证了所提方法的可行性和有效性。

An efficient approach based on wavelet transform is presented to detect the variation degree of dynamic background. Firstly, some candidate regions can be obtained by processing IR data through wavelet transform, then the degree of the difference between current and referenced background can be judged through detecting the difference of the above feature regions. Finally, some small moving candidate targets can be detected by eliminating background. After eliminating background, data association and robust tracking of those targets can be realized by level set tracking method based on region-based active contour with dynamic priors. Dynamic priors include shape descriptor, intensity information, moving features and etc. In addition, targets’ merging and splitting phenomena occasionally arise during multi-target tracking. This problem can be solved by ‘memorizing and filling’. The proposed approach is validated to track multi-target effectively by using actual infrared image sequences with complex dynamic background. Experiment results indicate the feasibility and effectiveness of the proposed method.