结合卷积神经网络与曲线拟合的人体尺寸测量

目的 人体尺寸测量是服装制作中的一个重要环节。非接触式人体测量具有效率高、方便快捷的优点，但存在测量精准度较低、对设备和外界环境要求高等问题。为进一步改进这些问题，本文基于卷积神经网络建立模型，相继提出人体分割和关键点检测的方法、基于Bezier曲线的人体肩宽测量方法和基于双椭圆拟合的人体围度测量方法。方法 通过摄像头获取人体的正面、侧面及背面图像；利用Deeplabv3+算法对人体图像进行分割获得人体轮廓，利用OpenPose算法对人体关键点进行检测及定位，利用肩部端点处的角度特征并结合人体肩部关节点信息确定肩部端点，利用肩部曲线与Bezier曲线的相似性通过计算肩部Bezier曲线的长度得到肩宽，通过关键点信息确定胸围、腰围及臀围的宽度和厚度，并建立围度曲线的双椭圆拟合模型，采用线性回归法训练得到拟合模型中的参数，最后利用双椭圆拟合曲线的周长得到人体围度。结果 根据本文方法对100位被测者进行肩宽计算，对132位被测者进行人体围度计算，平均绝对误差均在3 cm以内，符合国家测量标准，且整套方法操作方便，结果稳定。结论 实验验证了本文方法在人体尺寸测量中的精度，降低了非接触式人体测量法对外界环境和设备的依赖程度，提高了系统的鲁棒性，为非接触式人体测量走向实用化打下了坚实基础。

The convolution neural network and curve fitting based human body size measurement

Objective The human body size measurement is developed in garment making based on contact and non-contact methods. The manual contact measurement mainly uses soft ruler and other tools to measure by hand. This method is time costly and inaccuracy, which is not appropriate for large-scaled human body size collection. The intelligent non-contact human body measurement obtains human body size through some equipment and instruments, which has the features of high efficiency, easy use and quick response. Traditional non-contact human body measurement is constrained of external factors, such as single color background and fixed lighting scenario. In addition, the traditional methods extract less number of human key points, or the position of human key points have some deviation for the human body with particular size. To further address these issues, we construct a convolution neural network model and facilitate the method for segmenting human body and detecting the key points. Meanwhile, we focus on the measurement method of shoulder width based on Bezier curve, as well as the measurement method of body circumference based on double ellipse fitting. Method Three images of the front, side and back of the human body are captured through the camera. The acquired image is segmented using the Deeplavb3+ algorithm and the human body contour is obtained. Lightweight OpenPose algorithm is employed to detect the 13 human body key points, including shoulder joint, elbow joint, wrist joint, hip joint, knee joint, ankle joint, etc. First, the end point of shoulder is identified by integrated information of human shoulder joints because the angle between the tangent lines at the end of shoulder is less than the angle of its surrounding point. The two endpoints of shoulder curve can be regarded as the starting point and the ending point of the quadratic Bezier curve based on the similarity of shoulder curve and Bezier curve. Next, the intersection of the tangent lines of the two ends of shoulder width curve is used as the control point of the quadratic Bezier curve. The shoulder width is obtained by the calculated length of shoulder Bezier curve. The range of waist is determined by the key points of hip joint and elbow joint in the human contour curve. The average waist width and thickness within the range are taken as the width and thickness of the human waist. The waist curve of human body is as two ellipses with equal long axes and unequal short axes. The double ellipse fitting model is established for human circumference curve. The parameters of the double ellipse fitting model are trained by linear regression method. Finally, the waist length of human body is obtained with the circumference of the double ellipse fitting curves. Similarly, the measurement position of chest and hip circumference is roughly determined in accordance with the results of human segmentation and key detected points. The measurement curve of the whole chest and hip circumference is fitted to obtain the curve length. Result we compare the human key point detection performance of three algorithms, including Lightweight OpenPose, contour-based detection, and human proportion based detection. Our experimental results show that Lightweight OpenPose can extract human key points more accurately.Additionally, we also compare the network results and computation of OpenPose to Lightweight OpenPose. The results show that Lightweight OpenPose can simplify the computation and guarantee the detection accuracy. We select 100 shoulder categories for data evaluation of the measurement of shoulder width, including flat, wide or narrow, and sliding one. We compare the shoulder width measurement performance of three algorithms, including vanishing point and proportion based algorithm, regression analysis and the proposed algorithm. These experimental results demonstrate that our average absolute error of shoulder width measured is less than 2 cm. For the measurement of human circumference, 132 samples are selected for data evaluation. For the measurement of waist circumference, we evaluate four methods related to regression analysis method, support vector machine (SVM) -based waist circumference estimation algorithm, direct ellipse fitting method and the proposed algorithm. For the measurement of chest and hip circumference, we carry out the evaluations of three methods like regression analysis method, direct ellipse fitting method and the proposed algorithm. The comparative results show that the average abstract error of the proposed algorithm is within 3 cm, which is suitable for the national measurement standard. Conclusion Our algorithm evaluate the accuracy of human body size measurement, which reduces the dependence of the non-contact measurement on the external environment and equipment, improves the robustness of the system, and promotes the non-contact measurement in practice. The future research potentials can be improved through the robustness of human body segmentation method, the elimination of hair or other racial bodies on shoulder width and chest circumference measurement, and the detection accuracy of shoulder endpoint and chest positioning. It is challenged that more integrated human body data like chest, waist or hip contour need to be estimated for potential features of individual-based human body size further.