## 1 Introduction  
Currently, thermal imaging cameras are widely used in many fields [1] and have obvious advantages over visible light in intelligent surveillance. Thermal imaging cameras rely on the heat emitted by the object itself to take pictures, which is more suitable for surveillance scenarios that require privacy protection and temperature measurement. Although thermal and visible light images are vastly different in terms of imaging principles, the research idea of behavior recognition is similar. Figure 1 shows an example of a visible light image and thermal imaging image. Figure 1: Examples of imaging effects. (**a**) Visible light image. (**b**) Thermal imaging image taken in the same scene as (**a**). (**c**) Image of a person suddenly falling down. Traditional video behavior recognition methods are mainly based on visual feature extraction or motion feature extraction. One of the methods based on visual feature extraction is mainly by sampling video frames, manually designing features that can characterize actions for the sampled points, and then encoding and processing them for action classification by a classifier. Such methods mainly include two different types of feature extraction: global feature-based [2; 3] and local feature-based [4; 5] methods. The method based on motion feature extraction mainly uses optical flow fields to extract motion trajectories and then encodes the features for training to complete the classification. One of the most robust algorithms is the improved dense trajectories (iDT) algorithm [6; 7]. With the improvement in computer performance, behavior recognition methods based on deep learning have become popular. There are three main types of mainstream methods: dual-stream networks [8; 9; 10], 3D convolutional networks [11; 12; 13], and behavior recognition methods based on skeletal keypoints [14; 15; 16; 17], which have emerged in recent years. Among them, the behavior recognition method based on skeletal keypoints shows excellent results by expressing the spatial structure of different behaviors with the help of graph convolutional networks. The literature [14] proposed the ST-GCN algorithm based on the OpenPose algorithm. [PERSON] et al. [17] first used the target detection algorithm to obtain the human bounding box and then used the AlphaPose algorithm to obtain the sequence of keypoints, and then classified the predicted behavior. We found that most current thermal imaging cameras are fixed-position gun cameras rather than rotating dome cameras with gimbals. The video resolution is generally low, which leads to the inability to maintain high accuracy and robustness even with the skeletal keypoints-based approach. In this work, we work on the implementation of a method for human behavior recognition in low-resolution thermal imaging video based on fixed scenes, called the behavioral parameter field (BPF). The contributions of this paper are as follows:  
(1) The BPF algorithm based on a low-resolution thermal imaging camera is proposed to learn the behavioral features of the human body using features, such as aspect ratio, height, velocity, acceleration, the center of mass, and orientation angle, and then the stability and validity of the behavioral parameter field are ensured by a similarity update strategy. The feature matching method based on intersection and ratio is proposed to identify abnormal behavior. (2) Considering the high real-time requirements of the surveillance task, the single-stage YOLOv4 [18] algorithm is selected as the baseline in this paper. The effects of three variants of the scSE attention mechanism [19] embedded into the backbone of the model on the performance of the algorithm are studied and analyzed separately to enhance the information representation capability of the YOLOv4 algorithm for small target feature maps.