摘要： 传统非接触式人体尺寸测量中的关键特征点是根据人体各部位的比例关系直接提取，该方法对人体体型和着装要求严格，导致在多数情形下获取的关键特征点存在较大误差。为此，提出一种基于自适应人体结构分割（ABSS）的着装人体多特征点提取和尺寸测量算法Human pesm-abss。分析东西方人体的异构性和自身体型的差异，利用ABSS对人体结构关键区域进行分割。针对颈、肩部位特征点的提取，给出最大距离法和局部最大曲率法，解决传统算法适应性差及鲁棒性弱的问题。对210组标准差较大样本的实验测量数据与真实尺寸信息进行对比分析，结果表明，Human pesm-abss算法相对于非闭合Snake和Simple-FCN-ASM模型，平均误差分别减少2.2 cm和0.26 cm，时耗分别缩短了1.098 s和3.552 s，具有更高的实时性与更强的鲁棒性，适用于在线批量人体着装尺寸测量。

关键词: HSV色彩空间, 非接触式测量, 特征点提取, 自适应人体结构分割, 轮廓检测

Abstract: Traditional non-contact body measurement methods extract key feature points directly based on the proportion of body parts,which tends to cause large errors for its strict requirements on body shapes and dresses.Therefore,this paper proposes an algorithm for multi-feature point extraction and measurement of dressed human bodies,Human pesm-abss,which uses Adaptive Body Structure Segmentation(ABSS).The algorithm analysis the differences of shape between Eastern and Western human bodies,and uses ABSS to segment the critical parts of human body structure.To extract the feature points in the neck and shoulder,this paper gives the maximum distance method and local maximum curvature method to solve the problem of poor adaptability and low robustness of traditional algorithms.The experimental data of 210 samples with large standard deviation is compared with the real size information.The results show that compared with the Unclosed Snake model and Simple-FCN-ASM model,the Human pesm-abss algorithm reduces the average error by 2.2 cm and 0.26 cm respectively,and reduces the time consumption by 1.098 s and 3.552 s.The algorithm has better real-time performance and robustness,and can find application in the online in-batch measurement of dressed human bodies.

Key words: HSV color space, non-contact measurement, feature point extraction, Adaptive Body Structure Segmentation(ABSS), contour detection

中图分类号: 

• TP391