A Computer Vision-Based Method for Vehicle Speed Detection Using Video Footage

The primary objective of this project is to create a web application for vehicle speed detection that will help law enforcement, traffic authorities, and private users measure vehicle speeds correctly from recorded footage. The goal of the project is to develop an affordable, easily usable speed monitoring tool that doesn’t require specialized gear, which will be especially helpful for law enforcement, traffic management, and accident investigation. The approach uses computer vision techniques with Python’s OpenCV module, integrating the Lucas Kanade optical flow algorithm to estimate speed from video data with the Haar Cascade algorithm for vehicle detection. With a user friendly interface that allows users to upload videos and obtain processed results with speed annotations, the system is based on the Django framework for the backend. User credentials and other fundamental data are stored in a database called SQLite, which guarantees data security and accessibility. For converting video formats to accommodate several input types, FFmpeg is utilized. Important findings show that this technology offers accurate speed estimates that are on par with those of specialized speed monitoring devices. The application makes it possible to upload videos, analyzes the film to identify cars, determines their speeds, and shows the user the annotated video. This makes it appropriate for both personal and real world traffic enforcement applications. Important findings show how useful this strategy is in situations where resources are scarce and real time monitoring systems are impractical. In some situations, this approach might be used in place of or in addition to current speed detection techniques, offering a productive, software based substitute for vehicle speed analysis.