## 4 Experiments
### Quantitative Comparison with Other Methods  
We completed training in IIAR-30 on the six behaviors (standing, walking, running, climbing, touch paddle, squatting) of the current mainstream methods in Table 2. The parameters of the model were counted. We used metrics such as GFOPS and FPS for evaluation in the GPU-3090 ti (NVIDIA, Santa Clara, CA, USA). and Jetson-Orin-AGX devices (NVIDIA, Santa Clara, CA, USA). The NC-ABLD framework achieves 67 FPS on the GPU-3090 ti and 15 FPS on the edge-side device Jetson-Orin-AGX inference despite having a high computational complexity of 98.7 GFLOPs. The code is avaliable at [[https://github.com/3083156185/NC-ABLD.git](https://github.com/3083156185/NC-ABLD.git)]([https://github.com/3083156185/NC-ABLD.git](https://github.com/3083156185/NC-ABLD.git)) accessed on 12 July 2024.  
The performance of SSD, Fast-RCNN, YOLOv3, YOLOv5-s, YOLOX-s, and the improved model used in this paper are compared. The evaluation metrics used are the average precision AP value and recall AR value introduced in the previous section. The metrics of the six models are compared, as shown in Table 3.  
As observed from the Table 3 data, the YOLOX-s target detection model demonstrates greater robustness compared to other popular target detection networks in terms of performance. It exhibits higher adaptability to the complex backgrounds found in airport field aprons and targets with significant scale variations. The improved model achieved a 1.7%  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **GFLOPs** & **Backbone** & **FPS/GPU-3090 ti** & **FPS/Jetson-Orin-AGX** \\ \hline DCAN & 446.2 & TSN & 29 & 7 \\ R-C3D & 455.3 & C3D & 24 & 9 \\ MP-Abr & 113.2 & LSTM & 46 & 11 \\ NC-ABLD & 98.7 & HRNet & 67 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of NC-ABLD computational complexity.  
increase in AP value compared to the baseline model, and the recall rate was improved by 1.2%.  
Based on the final predictions of human keypoint detection, a coordinate error analysis was conducted, and the results are presented in Table 4. The table includes the minimum, maximum, and average error values for the 13 skeleton points. The error values represent the absolute pixel Euclidean distances between the actual and predicted values. From Table 5, it can be seen that the prediction value of the detection of human keypoints has a low error compared to the true value.  
When recording abnormal behavior, we always pushed back time nodes in time according to the length of the sliding window intervals, and we used the time nodes obtained from the pushed back time nodes as the final prediction results. We take the error between the prediction result and the real time node as the evaluation index, and the six sets of experimental results obtained are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Skeletal Point** & **Minimum** & **Maximum** & **Average** & **Skeletal Point** & **Minimum** & **Maximum** & **Average** \\ \hline Nose & 0.12 & 21.89 & 2.59 & Left buttock & 0.08 & 44.65 & 8.47 \\ Left shoulder & 0.17 & 23.16 & 3.92 & Right buttock & 0.26 & 45.82 & 9.32 \\ Right shoulder & 0.22 & 23.46 & 2.92 & Left knee & 0.15 & 28.06 & 3.83 \\ Left elbow & 0.29 & 28.73 & 5.17 & Right knee & 0.33 & 29.97 & 3.18 \\ Right elbow & 0.22 & 35.53 & 5.19 & Left ankle & 0.17 & 34.41 & 2.96 \\ Left wrist & 0.09 & 30.11 & 4.48 & Right ankle & 0.28 & 33.89 & 2.92 \\ Right wrist & 0.19 & 31.15 & 3.71 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error statistics of the predicted results and labels of the skeleton points of the model in this paper (unit: pixels).
The experimental results show that the average error of the temporal node localization of abnormal behavior in the field was 0.4 s, which was almost equivalent to the ability of human eyes to identify abnormal behavior.