This research presents a comprehensive framework for implementing a vision-based Obstacle detection model system system aimed at enhancing security and efficiency in various domains such as car parking, vehicle monitoring, crime prevention, and public safety. The proposed system leverages advanced computer vision techniques to detect and classify vehicles in real-time, facilitating rapid identification, monitoring, and interception of suspect cars. The framework encompasses key components including data acquisition and preprocessing, feature extraction, training a classifier, and predicting new images using a combined approach of global feature representation and local feature extraction. The study outlines the significance, methodology, and benefits of the research work, emphasizing its potential to improve security measures and contribute to civil security enhancement.