摘要： 利用经典混合高斯模型进行目标检测存在耗时长、复杂度高的缺点,并且对噪声、光照和突发运动等干扰比较敏感。为此,提出一种改进的运动目标检测算法。基于混合高斯模型和六帧差分算法获得检测目标的基本轮廓,采用不同区域更新率的自适应选择策略提高算法准确性,同时通过形态学操作去除残余噪点,运用连通性检验提高检测目标完整性,获得轮廓较为完整清晰的运动目标二值化检测结果。仿真结果表明,该算法不仅能提高实时性,而且较好地解决了因目标状态变化、环境噪声以及光线变化等因素引起的误检问题。

关键词: 运动目标检测, 混合高斯模型, 六帧差分, 自适应选择策略, 运动目标二值化

Abstract: Aiming at the shortcomings such as time consuming,high complexity and sensitivity to noise,illumination and the sudden moving of objects in the traditional mixed Gaussian model algorithm for object detection,an improved moving object detection algorithm is presented in this paper.Based on the mixed Gaussian model and six-frame difference algorithm,the fundamental outline of detecting target is established.The accuracy of the algorithm is improved by using an adaptive selection strategy with the update rate for different regions.By means of the morphological operations,the residual noises are removed and the integrity of the detected target is improved through connectivity test.Then the binarization detection results of moving target with clearer and more complete outline are obtained.Simulation results show that the preposed algorithm not only improves the real-time performance of object detection,but also solves false detection problems caused by the change in object states,the environmental noise and the illumination changing,etc.

Key words: moving object detection, mixed Gaussian model, six-frame difference, adaptive selection strategy, moving object binarization

中图分类号: 

• TP18