We need to train a YOLOv8 model to monitor customer cameras and detect suspicious behavior in real-time. The goal is to create a robust object detection model that can identify any unusual or harmful activities captured by the cameras. Here are the key tasks involved:
Dataset Preparation:
Collect and prepare a dataset that includes labeled images of suspicious behaviors or activities. The dataset should include common scenarios like theft, vandalism, unauthorized access, etc. Training YOLOv8 Model:
Train the YOLOv8 object detection model using the prepared dataset. The model should be fine-tuned to accurately detect these behaviors under different lighting conditions, angles, and camera setups. Utilize transfer learning techniques to boost training speed and accuracy if necessary. Model Evaluation:
Evaluate the model using appropriate metrics (e.g., mAP, precision, recall) to ensure it is capable of detecting suspicious activities accurately. Perform hyperparameter tuning and experimentation to optimize the model’s performance. Deployment:
Once the model is trained and evaluated, deploy it into the customer camera monitoring system for real-time inference. Ensure the model can process video streams from multiple cameras simultaneously and trigger alerts or actions when suspicious activity is detected. Integration with Dashboard:
Integrate the model with a dashboard that can display alerts, the detected suspicious behavior, and recorded video clips for review. Ensure the dashboard updates in real-time and provides easy access to view detected events. Model Retraining:
Set up an automated retraining pipeline to periodically improve the model using new data (e.g., additional footage from cameras with labeled events).