一种基于机器学习的运动目标阴影检测新方法

运动目标检测是物联网智能视频监控中的一个重 要环节。通过对各种阴影检测方法的研究,我们发现仅仅通过一种 特征进行处理并不能准确的检测出阴影。因此,本文提出了一种基于机器学习的运动目标阴 影检测新方法。该方法深度 融合颜色信息、光学不变性以及纹理特征等因素,通过机器学习,自适应地进行目标阴影检 测,在综合分析三种信息检 测结果的基础上,实现对阴影的有效确定。该算法能够有效地结合各种方法的优势,在物联 网应用中取得了较好的效果 和运行效率。

A new method for shadow detection of moving object based on machine learning

Under the banner of internet of things (IOT),intelligent video survei llance is an emerging research direction in the field of computer vision.It focuses on how to describe,analyze and unders tand the contents of the surveillance video by using image and video processing technology and artificial intelligence techniqu es.Then it can control the video surveillance system according to the analysis results,making the video surveillance system h ave a higher level of intelligence.Moving object detection is an important part in intelligent video surveillance of IOT.B ut there are drawbacks on the illumination change,complex background and shadow in the traditional methods.B ased on the variety of shadow detection methods,we find that only using one feature can not obtain the result of det ection accurately.Then we present a new method of shadow detection for moving object based on machine learning,which co ntains color information,the invariance of optical and texture features.Through machine learning,it can adaptively do s hadow detection for moving object.Based on the comprehensive analysis of the detecting results of three kinds of informatio n,the shadow can be effectively determined. The method has combined the advantages of various methods,and it can get ideal e ffect and efficiency in the application of IOT.