###### Abstract  
With the widespread deployment of video surveillance devices, a large number of indoor and outdoor places are under the coverage of cameras, which plays a significant role in enhancing regional safety management and hazard detection. However, a vast number of cameras lead to high installation, maintenance, and analysis costs. At the same time, low-quality images and potential blind spots in key areas prevent the full utilization of the video system's effectiveness. This paper proposes an optimization method for video surveillance system deployment based on space syntax analysis and deep reinforcement learning. First, space syntax is used to calculate the connectivity value, control value, depth value, and integration of the surveillance area. Combined with visibility and axial analysis results, a weighted index grid map of the area's surveillance importance is constructed. This index describes the importance of video coverage at a given point in the area. Based on this index map, a deep reinforcement learning network based on DQN (Deep Q-Network) is proposed to optimize the best placement positions and angles for a given number of cameras in the area. Experiments show that the proposed framework, integrating space syntax and deep reinforcement learning, effectively improves video system coverage efficiency and allows for quick adjustment and refinement of camera placement by manually setting parameters for specific areas. Compared to existing coverage-first or experience-based optimization, the proposed method demonstrates significant performance and efficiency advantages.  
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cover the target area. In such cases, it is vital to select suitable positions in the surveillance scene to enhance the overall system's effectiveness, requiring a more detailed multi-metric analysis of the target area.  
This paper proposes an optimization framework for video surveillance systems based on space syntax analysis and reinforcement learning. First, the spatial relationships of the monitoring scene are analyzed. Using spatial syntax theory, the space is segmented, and different types of spatial descriptors are calculated, including convex space connectivity, control value, depth value, integration, visibility, and axial connectivity. The results are described using a grid map to calculate the importance value of locations within the monitored space. Based on this importance value, multiple cameras are deployed in the scene, and the corresponding total value of the field-of-view coverage is calculated as the score of the deployment strategy. Reinforcement learning is then used to iteratively move camera positions and calculate corresponding scores, building a value evaluation network to derive the optimized camera deployment strategy for the scene. Finally, the optimization scores corresponding to different numbers of cameras are computed to determine the required number of cameras and their deployment strategy for a given scene.  
The rest of this paper is structured as follows: Section 2 introduces the related work, Section 3 describes the proposed method in detail, Section 4 illustrates the experiment results, and Section 5 concludes the whole paper.