Enhancing the automatic target recognition function of the monitoring system to achieve concealed long-term monitoring is a complex task that involves many technical and ethical considerations. The following are some feasible ways and main points with the help of artificial intelligence.： ### Data collection and preprocessing 1. **Diversified data collection** -Collect a large number of surveillance video data of different scenes, different lighting conditions, and various targets at different angles. For example, it covers a variety of scenarios such as urban streets, indoor places, and field environments to improve the adaptability of the model to different environments. -Data sources can include existing surveillance camera networks, specially recorded test data sets, etc. 2. **Data annotation** -Accurately mark the collected data, and clearly mark the category, location, attitude and other information of various targets. For example, for personnel targets, mark their body contours, facial features, etc.; For vehicle targets, mark key information such as vehicle model and license plate number. - Use professional labeling tools and teams to ensure the accuracy and consistency of labeling. 3. **Data preprocessing** -Preprocess the original video data, including normalization, cropping, enhancement and other operations. For example, the pixel values of the image are normalized to a specific range to improve the efficiency and stability of model training; the target area is highlighted by cropping to reduce background interference; image enhancement techniques (such as histogram equalization, gamma correction, etc.) are used to improve image quality and enhance target features. ### Choose the right artificial intelligence algorithm 1. **Deep learning algorithms** -**Convolutional neural Network (CNN)**： -CNN is currently one of the most widely used algorithms in the field of automatic target recognition. It automatically extracts the characteristics of the image through the convolutional layer, and can effectively process the image data. For example, the classic AlexNet, VGG series, ResNet and other models have achieved good results in target recognition tasks. - You can choose the appropriate CNN architecture according to the specific needs and hardware resources of the monitoring system. If the computing resource requirements are high and the pursuit of high precision, you can choose a deeper network such as ResNet; if you need to run on a resource-constrained device, you can choose a lightweight model such as MobileNet. -**Recurrent neural network (RNN) and its variants**： -RNN and its variants (such as LSTM, GRU) are very useful for processing monitoring data with time series characteristics (such as personnel behavior trajectories, vehicle movement patterns, etc.). They can capture long-term dependencies in the sequence and help to analyze the dynamic behavior of the target more accurately. -For example, by analyzing the walking trajectory pattern of a person over a period of time, RNN is used to determine whether their behavior is abnormal, so as to achieve more intelligent monitoring. 2. **Migration learning** -Use a model pre-trained on a large-scale image data set (such as ImageNet) to migrate the parameters of some of its layers to the monitoring target recognition model. This can greatly reduce the time and amount of data required for model training and improve training efficiency. -For example, first freeze the first few layers on the basis of the pre-trained ResNet model, and then fine-tune the training for the target category in the monitoring data to make the model quickly adapt to the characteristics of the monitoring scenario. ### Model training and optimization 1. **Training strategy** -**Batch training**: Divide the marked data into multiple batches for training, train one batch of data each time, and gradually update the model parameters. This can effectively use memory and improve training efficiency. -**Iterative training**: The training process is repeated many times, each training is initialized with different parameters, and the optimal model parameters are selected by comparing the performance of the model on the verification set. Generally, a suitable number of iterations is set, such as hundreds or even thousands of times. 2. **Loss function selection** -Select the appropriate loss function according to the characteristics of the target recognition task. Commonly used is the cross entropy loss function, which is suitable for multi-classification problems and can measure the difference between the model's prediction results and the real label, and guide the model to optimize in the right direction. -For some situations where the accuracy of the target's position needs to be considered, the regression loss function (such as mean square error loss) can be combined to enable the model to more accurately locate the target's position in the image while identifying the target. 3. **Model evaluation and optimization** -Use verification sets and test sets to evaluate the trained model. The performance of the model is measured by calculating indicators such as accuracy, recall rate, and F1 value. -Analyze the problems of the model based on the evaluation results, such as the low accuracy of recognition of certain categories and poor adaptability to specific scenarios. Then adjust the model structure in a targeted manner, optimize the training parameters, or add data for improvement. For example, if the model is found to have a poor effect on target recognition under night light conditions, you can increase the night data for retraining, or adjust the image enhancement strategy to better adapt to the night scene. ### Enhance concealment and long-term operation ability 1. **Low-power design** - Choose a low-power hardware platform, such as an embedded device based on the ARM architecture, to reduce the energy consumption of the monitoring system during operation and reduce heat generation, so as to achieve long-term stable operation. -