Abstract
This work presents the development of a computational solution for intelligent home monitoring, designed to detect people, analyze their behaviors and issue alerts for abnormal activities. YOLOv8-Pose technology was used to detect people's key points and a modified MLP (Multilayer Perceptron) neural network with LSTM (Long Short-Term Memory) characteristics was used to classify behavior. The training dataset was created by recording videos using a smartphone strategically positioned to capture the frontal area of a residence. This resulted in 154 video clips where the actor exhibited normal behaviors by walking along the scene and 154 videos where the actor performed actions that could lead to home invasion, such as attempting to climb the fence or break the garage gate. To carry out the experiments, videos with multiple actors and different behaviors were analyzed to evaluate the effectiveness of the developed methodology. The results indicated a high success rate in detecting normal behaviors, although challenges remain in scenarios with partial occlusion. The model's accuracy in classifying behaviors was 91.6%, reflecting its effectiveness in correctly identifying normal and abnormal activities.
Published Version
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