CAJ | 학술논문

提出了一种基于视频人体运动状态判决的的跌倒检测方法，该方法由运动目标检测、目标运动跟踪和目标运动行为识别三部分组成。在运动目标检测方面采用两次目标框选策略提高目标检测精度；利用目标运动轨迹的连续性，具体为利用上一帧运动物体的中心坐标信息来降低目标跟踪的计算复杂度。采用两级支持向量机( SVM)决策的方法实现目标运动行为的识别：第一级SVM分类器利用高宽比等运动物体特征将人体的直立姿态与非直立姿态进行区分；第二级SVM分类器利用Zernike矩特征等特征将人体的跌倒状态从非直立状态中区分出来。初步实验测试表明所提出的跌倒检测算法的性能与光照条件、跌倒方式、摄像头的架设方式均有密切关系，平均正确检测率为88．7%。
제출료일충기우시빈인체운동상태판결적적질도검측방법，해방법유운동목표검측、목표운동근종화목표운동행위식별삼부분조성。재운동목표검측방면채용량차목표광선책략제고목표검측정도；이용목표운동궤적적련속성，구체위이용상일정운동물체적중심좌표신식래강저목표근종적계산복잡도。채용량급지지향량궤( SVM)결책적방법실현목표운동행위적식별：제일급SVM분류기이용고관비등운동물체특정장인체적직립자태여비직립자태진행구분；제이급SVM분류기이용Zernike구특정등특정장인체적질도상태종비직립상태중구분출래。초보실험측시표명소제출적질도검측산법적성능여광조조건、질도방식、섭상두적가설방식균유밀절관계，평균정학검측솔위88．7%。
A human body falling detection method based on posture recogniton was proposed, which is consisted of three stages: moving target detection, target moving tracking and identification of behavior status. The performance of moving target detection was improved with two level marquee target detection. And continuity of body movement was used to reduce the computation complexity, in which the coordinates of previous flame was utilized. A two-level Support Vector Machine ( SVM) classifier was designed to detect body falling, in which human body posture was classified into up-right or not first with features such as rate of height to width of the moving target; And body falling was selected out from the non-up-right postures with features such as Zernike moments. The experimental results show that the performance correlates closely to lighting condition, the posture of camera and human body falling pattern, and the average accuracy of falling detection is 88. 7%.