CAJ | 학술논문

为高效提取生猪的行走快慢特征，以微软公司Kinect作为图像采集设备，采集生猪运动深度图像序列。在对各帧深度图像进行骨架提取、剪枝的基础上，采用基于路径相似性骨架图匹配法确定生猪前后肢骨架端点，进一步以骨架端点所属骨架枝子集像素值特征判定端点远近侧属性。以生猪前后肢远、近侧端点的帧间相对坐标变化建立了生猪运动模型，提出了通过帧间坐标变化点集拟合正弦曲线计算生猪行走完整步的方法。最后，通过计算序列完整步与序列采集时间长度比值提取生猪步频特征。通过对采集的28个生猪运动深度图像序列及其镜像序列共56个图像序列进行的试验，表明该文提出方法的正确率达到82.1%。该项研究对于开展生猪异常步态分析，进一步建立生猪多源特征融合的计算机视觉异常监测系统，提高生猪异常行为预警可靠性具有重要意义。
위고효제취생저적행주쾌만특정，이미연공사Kinect작위도상채집설비，채집생저운동심도도상서렬。재대각정심도도상진행골가제취、전지적기출상，채용기우로경상사성골가도필배법학정생저전후지골가단점，진일보이골가단점소속골가지자집상소치특정판정단점원근측속성。이생저전후지원、근측단점적정간상대좌표변화건립료생저운동모형，제출료통과정간좌표변화점집의합정현곡선계산생저행주완정보적방법。최후，통과계산서렬완정보여서렬채집시간장도비치제취생저보빈특정。통과대채집적28개생저운동심도도상서렬급기경상서렬공56개도상서렬진행적시험，표명해문제출방법적정학솔체도82.1%。해항연구대우개전생저이상보태분석，진일보건립생저다원특정융합적계산궤시각이상감측계통，제고생저이상행위예경가고성구유중요의의。
To further research the extracting method of the pig gait features, the paper firstly focus on the extraction of the pig gait frequency. A gait frequency extraction method was proposed based on analyzing the skeleton endpoints of depth image. Firstly, a series of processes, including skeleton extracting and pruning, were taken to the frames of depth image sequences. Secondly, a path similarity skeleton graph matching method was introduced to distinguish the fore-leg endpoints and the hind-leg endpoints from the skeleton graph. Then considering the characteristics of the depth image, a rule to distinguish the far-side endpoint and the near-side endpoint was constructed by calculating the average value of neighbor skeleton points of the endpoint. After ascertaining the skeleton endpoints of four legs, a variable was defined to represent the relative position of the far-side endpoint and the near-side endpoint, along the horizontal direction between frames in a sequence. Furthermore, the fitting sine curves were used to represent the variations of the fore-leg endpoints and the hind-leg endpoints separately. At last, the reciprocal of the fitting sine curve frequency can be calculated and the INTPART of double reciprocal was regarded as the fore-leg steps (FS) or the hind-leg steps (HS). The complete step (CS) was defined as the minimum of FS and HS. The finally gait frequency can be calculated by using the CS value to divide the duration of the sequence. To verify the proposal method, 28 depth image sequences of pig moving were acquired by using the KINECT depth camera, at the Rongxin pig farming of Danyang city in Jiangsu province, China. Another 28 sequences were achieved by mirror transforming along the horizontal direction to the native sequences. Experiments were taken for all the 56 sequences by using the proposal method. Experimental results show that the success rate of the method proposed in this paper is 82.1%, up to 92% for the situation when the pig moves continuously and the moving directions is perpendicular or nearly perpendicular to the axis of the depth camera only. Incorrect results often appear when the pig stays for a long time between steps or by non-cross steps, it needs to further adapt the proposal method. For the situation of rough variation of the pig body occurring in the sequence, the proposal method is not suited because the matching of skeleton points can not be achieved. That is the insufficient point of the proposal method. The proposal method would help to carry the further research of the abnormal gait of pig and construct the abnormal monitoring system by fusion of multi-source vision features.