Abstract: The existing camouflage effect evaluation is mainly for a single still image, which can not simulate the process of the reconnaissance personnel's interpretation of a target. A feature statistics-based dynamic camouflage effect evaluation method is proposed based on mean shift target tracking algorithm. The proposed method is to establish a normalized joint Gaussian distribution by using the data of correlation features between the target and the background of eight-way domain, and use the distribution range of probability density to evaluate the camouflage effect of target. A logarithmic amplification probability is proposed for calculating the probability density of joint distribution, which solves the problem that the high-dimensional joint distribution probability density has low numerical sensitivity and is inconvenient to set a threshold. At the same time, a sample update strategy is introduced to make the sample library update randomly according to a certain probability, so as to better adapt to the change in large-scale background caused by the turn of seasons and other factors. In the experimental process, the first-level, second-level and third-level camouflages are applied to a certain command vehicle. After collecting the data, the logarithmic amplification probability is calculated, and the statistics on the curves is made. The results show that 3 camouflages divided in reality completely correspond to the first-level，second-level and third-level camouflages pre-set by the 3σ criterion. The experimental results show that the model can effectively reflect the camouflage effect of target. Key

Key words: camouflageeffect, effectevaluation, featurestatistics, probabilitydensity, Gaussiandistribution, targettracking