To obtain the data of noncontact measurement of the human body, the depth camera is used to collect the human body, and the obtained initial data are transformed into the required point cloud data for processing through coordinate transformation, and then the collected three‐dimensional point cloud data are preprocessed. The preprocessing includes point cloud downsampling, point cloud filtering, plane segmentation, outlier removal, point cloud surface estimation, and so forth. A new solution for point cloud filtering is proposed, which combines sliding least squares and unification and radius filtering. Compared with the traditional filtering, the effect is smoother, and finally the complete outline of the human body is obtained, and then the human body is measured. The results show that the human body data measured by this scheme is within the range of the relevant standard measurement accuracy.

To reduce the cost of virtual try‐on, a method of image deformation by body part size is proposed for the traditional two‐dimensional virtual try‐on method, which is challenging to represent the personalized characteristics of the body size of the fitting subject. On the basis of the input information of the user's body size, the method can generate a fitting effect that shows the user's characteristics with the corresponding clothes. The image segmentation algorithm is used to snap out the garment from the background garment image, and then the size and position of the garment are adjusted according to the dressing position of the standard mannequin image and fit the mannequin image. The final mesh with dense vertices is generated using surface subdivision. The experimental results show that this method can show good results in user personalized try‐on.

To identify whether the actual work trajectory of workers in the factory meets the predetermined work trajectory requirements, we proposed an efficient and accurate work trajectory similarity matching method. We comprehensively considered the similarity between the actual work track and the predetermined track from the two characteristics of track angle and track distance. Among them, the similarity of track rotation angle is calculated using the improved longest common subsequence algorithm, and the similarity of track distance is calculated using the improved dynamic time warping (DTW) algorithm. Then the results of the similarity calculation of these two features are weighted. Finally, the weighted results are used to evaluate the similarity between the actual work track and the predetermined track, so as to judge whether the actual work track meets the requirements of the predetermined track. Experimental data show that the trajectory similarity matching algorithm in this paper has higher accuracy and efficiency than traditional DTW and other algorithms, and has higher ability to resist the interference of trajectory point evacuation than traditional DTW and other algorithms.