# Faster R-CNN-LSTM Construction Site Unsafe Behavior Recognition Model
## 2 Method  
### Convolutional Neural Networks  
Convolutional Neural Networks (CNNs) have emerged as a cornerstone in the realm of deep learning [19; 20; 21]. Their prowess lies in processing data with a grid-like structure. A quintessential example of this would be images, where data can be visualized as a two-dimensional grid made up of pixels. One of the core processes in a CNN is the convolution operation, designed to automatically extract a range of features from the input data [22]. The design of a CNN as a spatial stream is geared towards capturing spatial features within images, especially those pertinent to human dynamics and postures. The localized nature of convolutional operations allows for the detection of localized features in images, such as edges, textures, and shapes, which are instrumental in recognizing human postures. Through pooling operations, CNNs exhibit spatial invariance, ensuring that humans can be detected regardless of their position in an image. Multiple convolutional and pooling layers in CNNs facilitate multilevel feature extraction, ranging from rudimentary edges and textures to intricate human body parts and postures. Convolutional Neural Networks (CNNs) primarily comprise two processing stages: the feature learning phase and the classification phase. The feature learning phase is realized through a combination of convolutional and pooling layers. This phase is dedicated to extracting the most salient features from training examples. Subsequently, these extracted features are fed into fully connected layers. The final layer of a deep neural network typically consists of a fully connected layer combined with a Softmax classification network, producing a classifier with a number of classes equivalent to the requirements of the project. (1) This operation involves moving through the input data using a convolution kernel, based on a predefined step size, known as the \"Stride\". Each of these movements entails a convolution operation. To grasp this concept in a more mathematical sense, consider the following formula: In this paper, the VGG 16 model is selected in the spatial flow. The model uses a 16-layer deep network, including 13 convolutional layers and 3 fully connected layers. The convolution kernel is 3 \(\times\) 3, and the step size is 1. (2) The input layer serves as the initial layer in the CNN architecture, facilitating the introduction of data into the neural network. In Convolutional Neural Networks (CNNs), the primary role of the input layer is to receive and prepare raw grid-structured data, such as images. For colored images, the input layer typically handles a three-dimensional array with dimensions (height, width, and channel count). In the case of RGB images, there are three channels, representing red, green, and blue, respectively, while grayscale images consist of a single channel. Prior to propagation through the network, image data are commonly normalized, converting each pixel value to a range between [0, 1], which aids in expediting the model's convergence. The input of the network layer is video frame data with helmet feature point information. Firstly, the static feature of the human motion space attitude is extracted from the image containing feature points, and then, the hidden dynamic feature is obtained by the position transformation of the same joint point of two video frames under \(\Delta\)t time difference. The network diagram of the process is shown in Figure 1.  
Specifically, the spatial flow defines a single action as p in the process of extracting information, the total number of action sequences is \(T\), the individual time is \(t\) (\(\textbf{t}\in\textbf{T}\)), and its action feature is \(f_{i}^{p}(i)\). Now, the information obtained by the network layer in the feature extraction process is integrated, and the sequence values of each frame of action are aggregated and normalized; that is, the maximum and minimum values obtained during the change of the joint vector i of the action are integrated.