## 4 Experimental Results  
To test the proposed method, we constructed connectivity and field-of-view analysis results for specific positions in a grain depot's surrounding area, as shown in Figure 7. The spatial syntax analysis uses the DepthMapX v0.8.0 [18] software developed to calculate spatial syntax values. The DQN model is implemented with pytorch, and the hardware is a PC with an NVIDIA GeForce RTX 4090. First, the spatial syntax importance of the target scenario is calculated to generate a raster map in which each pixel value represents its spatial syntax value combined with convex analysis, visibility analysis, and axis analysis. In Figure 7, all attributes are normalized into 0 to 1 and visualized in a heat map in which the red represent 1 and blue represent 0. In Figure 7a, we can see that the bottom-right parts have higher visibility values but lower connectivity. Then, these indexes are integrated to determinate the final value of the pixel. Figure 6: Definition of the improved DQN framework. The analysis highlights the weights of key locations around the depot, which can be further refined by manually setting weights and incorporating semantic information to create a more comprehensive weighting scheme. For example, the weighting of entry areas can be increased to facilitate more detailed monitoring of incoming and outgoing vehicles. To enhance the model training speed, the generated weight grid map was further simplified by compressing all resulting images to a 50 \(\times\) 50 pixel size. A configuration of 15 cameras was tested to assess the corresponding deployment positions. The relationship between the total score obtained during model training and the number of training iterations is shown in Figure 8.  
It can be observed that, after multiple training iterations, the proposed model tends to converge, yielding the highest achievable score for the current scenario, thus optimizing camera placement. To evaluate the Q-network under various hyperparameter settings, we conducted ablation experiments, testing different values for learning rate, model layers, batch size, and reward discount factor (gamma). Table 1 presents the ablation experiment results. The baseline model is configured with a learning rate of 0.0001, four layers, a batch size of 32, and a gamma of 0.8. The ablation experiment results indicate that, for the given experimental environment--especially with the camera placement optimization objective--the baseline model parameters yield the optimal overall training outcome. The reinforcement learning method offers high flexibility, combining camera video parameters with spatial syntax analysis and semantic adjustment for monitoring scenes. This enables the handling of various potential states, including panoramic cameras, key  
Figure 8: DQN model training results: (**a**) loss; (**b**) prediction accuracy. Figure 7: Space syntax computation results: (**a**) visibility; (**b**) connectivity. point coverage, and trajectory continuity analysis. By adjusting certain parameters and retraining the model, new optimized analysis results can be obtained. In terms of training speed, since scene coverage data can be precomputed offline and shared via caching, the model's training time is relatively fast. Figure 9 presents the time required for the model training and optimization to stabilize under different pixel resolutions of the scene. It is shown that along with the increasing generated map size, the calculation time is expended quickly. Therefore, it is necessary to select a small-sized map, such as 50 \(\times\) 50. The overall size should be less than 100 \(\times\) 100 to achieve better performance. The model's camera placement positions and scores for different numbers of cameras are shown in Figure 10. In Table 2, we compared these scores with those provided by video monitoring installation personnel (Figure 10b). Under the spatial syntax scoring framework, the reinforcement learning-based approach achieved a higher score.