| 1. | Visual analysis of human motion has been receiving increasing attention from researchers in the fields of image processing and computer vision during the past few years . it has a lot of applications in virtual reality , smart surveillance system , advanced user interface , motion analysis and video compressing , etc . this paper focuses on the technology of human motion tracking based on video , first , we make a summarization of the domestic and overseas status of the research in this field . on the basis of this , we analyse the technical difficulties of human motion tracking . as most of the existing model - based methods of human motion tracking perform not so good in some situation as they need mannual intervention , and also the precision of tracking is not so satisfying during the research of tracking of walking people because of the self - occlusion of legs , this paper proposes an algorithm of automatic detection and tracking of legs of the walking people based on monocular image sequences , in which we analyse the features of walking people , track the five joints of lower limbs , get various parameters , and then re - construct the walking process . the main research achievement is as follows : 1 ) we propose an algorithm of markerless automatic extraction of leg skeleton . first we divide the video into continuous image sequences , after background subtraction , the satisfying human region could be extracted , then we get a single - connected region by converting the rgb image to binary image and median filtering . afterwards , the contour of lower limbs in the frame with a widest boundingbox is detected , using sobel operator , to find the ankle joint of leg behind according to the features and rules of walking , then , the joint of knee of leg behind , hip , ankle of leg in front , knee of leg in front could be got in turn . so , model of leg skeleton is constructed
首先将视频分解成许多连续的静态图像帧,经过背景去除,把感兴趣的人体区域提取出来,通过二值化,中值滤波等预处理方法得到只有人体的一个单连通区域,然后用sobel算子检测出boundingbox最宽帧中人体下半身的轮廓,根据运动规律及特征找到后腿踝关节点,结合从boundingbox最窄帧中所获取的腿长依次得到后腿膝关节,跨部关节,前腿踝关节,前腿膝关节四点,从而构建出腿部骨架模型。 2 )实现了人体步行腿部骨架的跟踪算法。在完成对腿部骨架模型的自动初始化之后,本文对跨关节、膝关节及踝关节分别采用运动建模、圆周相交定点算法、运动预测及预测点周围搜索rgb相似矩形块三种方法确定每一帧中其实际坐标,从而重构出腿部骨架的运动过程。 |
