With the help of artificial intelligence, the automatic target recognition function of the surveillance system can be enhanced in the following ways to facilitate covert and long-term surveillance: 1. **Deep learning training model**: Use deep learning models such as convolutional neural networks (CNN) for target recognition. These models can be trained with large amounts of data to improve the recognition accuracy of various targets (such as people, vehicles, animals, etc.). 2. **Real-time image processing**: Use efficient computing platforms (such as edge computing devices) for real-time image processing to reduce latency and respond quickly to potential events. This can help quickly identify and track targets during monitoring. 3. **Multi-modal sensor fusion**: Combine multiple sensor information such as traditional surveillance cameras, thermal imaging devices, and acoustic sensors to capture targets from different angles to improve the accuracy and reliability of recognition. Thermal imaging technology can provide additional information, especially in low-light conditions. 4. **Background modeling and change detection**: Use machine learning technology to build background models, monitor changes in the environment in real time, and identify abnormal behaviors or targets in a timely manner. This approach can reduce false alarms and improve the intelligence level of the system. 5. **Target tracking algorithm**: Achieve continuous tracking of targets so that monitored objects can be continuously recognized as they move. This can be achieved through technologies such as Kalman filtering and optical flow, which enable the system to better understand the target's activity pattern. 6. **Behavior analysis and anomaly detection**: Not only can target identification be performed, but target behavior can also be analyzed to determine whether there is suspicious activity. Through machine learning to train behavioral models, the difference between normal and abnormal behavior can be identified, thereby improving the intelligence of monitoring. 7. **Data reduction and compression technology**: Using data compression and encryption technology, the bandwidth and space requirements for moving and storing data are reduced while maintaining the recognition accuracy of the target. This is very important for the concealment of long-term monitoring. 8. **Intelligent alarm system**: The system can automatically generate alarms based on the results of identification and analysis, notify operators to pay attention to potential security incidents, and improve monitoring efficiency. 9. **User interface and visualization tools**: Develop a friendly user interface so that operators can more easily understand and use monitoring data. In addition, providing visualization tools can help operators monitor and identify targets more effectively. Through these methods, the automatic target recognition capability of the monitoring system can be greatly improved, providing effective support for covert and long-term monitoring. However, when implementing these technologies, it is also necessary to pay attention to data privacy and ethical issues to ensure that monitoring technology is used legally and compliantly.