## 4 Experiments
### Experimental Results and Analysis
#### 4.3.2 Experimental Testing of Abnormal Behavior Detection  
1. Comparison of experimental results of typical methods;  
The behavior recognition method based on skeletal keypoints has excellent applications on visible data sets. However, compared to the rich and diverse datasets in the visible domain, there are no large datasets in the thermal imaging domain, so it is not reasonable to train the pose estimation algorithm specifically to obtain thermal imaging human keypoints. Additionally, the accuracy of skeletal keypoints is crucial for the final behavioral classification. In this paper, AlphaPose and OpenPose are selected as the algorithms for skeletal keypoints extraction to compare with the proposed BPF method, and thermal imaging images in two scenes are used as the input for the experiments. The experimental results are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **FPS** & **[EMAIL_ADDRESS]/\%** \\ \hline SSD [26] & 28 & 96.93 \\ YOLOv3 [25] & 23 & 97.28 \\ YOLOv4 [18] & 22 & 97.97 \\ M2 Det [27] & 24 & 97.44 \\ RFBNet [28] & 27 & 93.98 \\ cSE-YOLOv4 & 22 & 98.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different algorithms.  
Figure 11: Comparison of results. (**a**,**b**) show that the extracted skeletal keypoints of AlphaPose and OpenPose in both scenes are not guaranteed to be accurate. Therefore, we can imagine the difficulty of the skeletal keypoint-based method for behavior recognition in this scene. Furthermore, (**c**) demonstrates that the method in this paper can still effectively extract the features required for the BPF (h is height, wh_r is the aspect ratio, and theta is orientation angle in (**c**)) and successfully detect the falling behavior.  
Update strategy ablation experiment;  
We test the impact of the behavioral parameter field established using different strategies on the performance of the two types of behavioral classification accuracy in the same scenario. The results are summarized in Table 3. We observe that similarity update strategies achieve better performance when the behavior parameter field is established for longer. This confirms the motivation of our design of this strategy which considers the relationship between the contact between different dimensions of feature \(B\) and the characteristics of the historical phase. Based on these observations, we use this similarity update strategy in the following experiments.  
3. BPF model performance test;  
After testing, the detection results of our proposed method for identifying abnormal behavior based on the BPF model for thermal imaging video on the constructed test set are shown in Table 4. Experiments show that the method of human abnormal behavior recognition based on the BPF model can effectively detect the abnormal behavior in the video taken by a fixed thermal imaging camera, which has good practical value.  
4. Sensitivity analysis of Hyperparameters;  
In the process of establishing the BPF model, feature \(A\) needs to accumulate \(F\) frames to calculate feature \(B\). By experimentally simulating the statistical results of elderly people's actions, the cumulative number of frames \(F\) is best when taken as 40. In updating the behavioral parameter field, \(N\) is best taken as 3 in the similarity update strategy. The parameters used in the experiments are: Jaccard coefficient threshold \(T_{ion2}=0.4\); normal behavior judgment threshold \(T_{si}=0.9\); horizontal acceleration judgment threshold \(T_{ax}=10\) and vertical acceleration judgment threshold \(T_{ay}=10\) in the fall event; and similarity threshold \(T_{ts}=0.95\) and prolonged immobility time judgment threshold \(T_{ina}=0.75\) in the prolonged immobility event. The values of the above parameters are obtained from extensive experimental verification and are used as the default parameters of the algorithm.