This paper presents a modified Gaussian Mixture Model (GMM) and Adaptive Thresholding designed to improve object detection accuracy for the outdoor surveillance. Intrinsic and extrinsic improvements in traditional GMM will handle the outdoor dynamic scenes e.g. tree weaving, gradual illumination changes, partial occlusions and also handles certain amount of shadow. For foreground detection, Adaptive Thresholding is utilized for better classification among the background and foreground objects and it also help to reduces false positives and hence increases the detection accuracy. We tested proposed algorithm on standard datasets consisting of CDnet 2014, PETS 2009 and ViSOR. The robustness of the propose algorithm has been compared with the ground truth and other similar approaches through several performance evaluation metrics. The experimental results conclude that the proposed algorithm efficiently detect objects in dynamic environments as well as handle partial occlusions and certain amount of shadows very efficiently.

本文提出了一种改进的高斯混合模型（GMM）和自适应阈值设计，旨在提高户外监视的目标检测精度。传统GMM的内在和外在改进将处理室外动态场景，例如树木编织，照明逐渐变化，局部遮挡以及处理一定数量的阴影。对于前景检测，自适应阈值用于在背景和前景对象之间进行更好的分类，还有助于减少误报，从而提高检测精度。我们在由CDnet 2014，PETS 2009和ViSOR组成的标准数据集上测试了建议的算法。通过几种性能评估指标，将该提议算法的鲁棒性与地面真实性和其他类似方法进行了比较。