Intelligent Surveillance System for Accident Detection and Alerts with Autonomous Object Recognition using Machine Learning

The Accident Detection and Alert System leverages Machine Learning (ML) to enable accurate, real-time identification of road accidents. The system integrates sensor data (accelerometers, gyroscopes) and video feeds to detect abrupt motion changes and collision patterns. By employing a combination of supervised and unsupervised ML methods, it ensures precise accident detection, minimizes false positives, and enhances emergency response times. Key technologies include deep learning frameworks like TensorFlow and PyTorch for video-based detection, as well as traditional ML algorithms like Random Forest for sensor data classification. The solution incorporates advanced preprocessing techniques, feature extraction, and model optimization to handle real-time processing challenges. Deployed via cloud services and edge devices, the system triggers GPS-enabled alerts, accelerating life-saving interventions. By addressing challenges such as sparse datasets, false positives, and computational demands, this ML-driven solution exemplifies the potential of AI in improving road safety and emergency management.