## 1 Introduction and Related Works
### Feature-Based Methods  
To achieve HAR through input videos, various artificial features or feature extraction methods are commonly used. They are mainly divided into two steps: feature representation and action classification [11]. Feature representation refers to representing the action information in the original video data as feature vectors, while action classification refers to using action feature vectors to achieve the classification of different action categories.  
For example, [PERSON] et al. [12] used Motion Energy Images (MEI) and Motion History Images (MHI) for encoding motion information into monochromatic images, where MEI describes the location of the action, and MHI describes the motion of the action by calculating the changes in pixels at the same location within a certain period of time. In order to remove visual angle influencing factors in feature representation, [PERSON] et al. [13] proposed a 3D Motion History Volume, which extracts three dimensional stereo pixels from multiple camera views, and then uses Fourier transform to generate motion representations that are not affected by position and angle. The above feature representation method captures the motion information of the entire motion subject, which is easy to introduce irrelevant information such as the motion subject and chaotic background. To alleviate this problem, researchers turned to feature representation of local regions with significant motion information. [PERSON] et al. [14] proposed a local feature representation method based on spatio-temporal interest points, using Harris Corner Detector to detect regions with the largest gradient changes in each direction. [PERSON] et al. [15] proposed using a 2D Gaussian smoothing kernel on the temporal dimension of each point of interest, and a 1D Gabor filter on the spatial dimension to extract the original pixel values, gradients, and optical flow features around the point of interest, and splice them to generate a feature vector. [PERSON] et al. [16] aggregate the optical flow features in the local region where the point of interest is located into a histogram to obtain a Histogram of Optical Flow (HOF), and combine it with the HOG feature [17] to characterize complex actions in this domain. Spatio-temporal interest points can capture the most significant features in a local area, but they cannot capture motion information in the temporal dimension. In order to capture temporal information of actions, [PERSON] et al. [18] proposed a feature trajectory method, which tracks points of interest and describes changes in the motion characteristics of the points of interest. [PERSON] et al. [19] searched for tracks by matching corresponding feature points on continuous video images, and used average descriptors to generate track representations. [PERSON] in [20] designed a simple statistical distance measure between video sequences which captures the similarities in their behavioral content.  
In summary, although traditional artificial feature methods rely too heavily on human experience and have unsatisfactory recognition accuracy for arbitrary actions, there are still advantages in the recognition of worker actions involved in this article, as worker operations typically follow various standards, which are different from arbitrary action recognition. Therefore, the method design in this paper combines some traditional methods of statistical extraction of artificial features.