## 5 Experimental Validation
### Binocular Camera Target Detection and Tracking  
The neural network adopted in this experiment was Yolo v5s, whose model profiles were identical to those of other Yolo v5 series networks, except for the depth_multiple and width_multiple. Yolo v5s boasts a one-deep depth and no module repetition, which enables a faster processing speed in large target recognition. The Yolo v5 loss function includes classification, localization, and confidence loss. The total loss function is the weighted sum of the three, and the confidence and classification loss in Yolo v5 are used. Binary cross entropy is used to process the information, and the positioning loss is determined by using the CIOU loss. (1) Yolo v5s training  
The dataset used in this experiment is the data with the label name \"person\" in the public dataset PASCAL VOC. To enhance the recognition ability of the model when analyzing densely populated environments, we added 9000 dense pedestrian pictures that we collected ourselves. The dataset size and the number of pedestrians are shown in Table 3.  
For the experiment, the dataset was divided into a training, verification, and test set in a ratio of 7:2:1. We selected the Faster-RCNN and Yolo v3, v4, and v5s neural networks for comparison. The detection accuracy and running speed of the neural network are shown in Table 4. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **mAP (\%)** & **FPS** \\ \hline Faster-RCNN & 58.9 & - \\ Yolo v3 & 55.0 & 35 \\ Yolo v4 & 65.4 & 33 \\ Yolo v5s & 70.5 & 40 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Test results of different neural networks on the dataset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Software and Hardware** & **Version** \\ \hline Operating system & Windows11 \\ CPU & i7-13700K \\ GPU & NVIDIA GeForce RTX 3080 Ti \\ CUDA & 12.0 \\ Pytorch version & 11.8 \\ Python version & 3.9 \\ Software & PyCharm2021 \\ MATLAB & R2022b \\ Binocular camera & ZED \\ Radar test board & IWR1642 boost \\ Real-time data capture module & DCA1000 EVM \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Equipment and software information. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Training Set** & **Number of Pedestrians** \\ \hline VOC & 17,125 & 4374 \\ Ours & 9000 & 3489 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental pedestrian dataset. After the iteration, the loss was about 0.04786, the final convergence value of the mAP was 0.70475, and the precision and recall converged at 0.79443 and 0.61314, respectively. The dense crowd test is shown in Figure 13.  
The model detection performance was high and met the experimental requirements. (2) DeepSORT multitarget tracking algorithm  
Existing mainstream target-tracking algorithms are based on a tracking-by-detection strategy; that is, they function in accordance with the detection results. This strategy is employed in DeepSORT to track a crowd in a multiperson environment, whereby the number marked on each box is a unique ID to identify a person. DeepSORT processes each frame as follows:  
Obtain bbox \(\rightarrow\) generate detections \(\rightarrow\) predict Kalman filter \(\rightarrow\) match predicted tracks with detections in the current frame using the Hungarian algorithm (cascade and IOU matching) \(\rightarrow\) Kalman filter update. (3) The tracking network was trained  
The market-1501 dataset was used for training; it was collected and publicly released in 2015 within Tsinghua University campus. It consists of 1501 pedestrians captured by six cameras, including five high-definition cameras and one low-resolution camera, which resulted in a total of 32,668 pedestrian-bounding boxes. Each pedestrian was captured by at least two cameras, and multiple images of the same person may have been taken with different cameras. The training set contained 751 individuals with 12,936 images with  
Figure 12: Loss. Figure 13: Dense pedestrian recognition test.