Publication details
he Vehicle Detection & Speed Detection Project is a computer vision project that involves
detecting and tracking vehicles in real-time from a video feed or CC Tv Camera. The
project utilizes deep learning algorithms, such as Convolutional Neural Networks (CNNs),
Object Tracking etc to detect the presence of vehicles in a frame and to locate their position.
Once the vehicles are detected, the project tracks their movement over time to estimate
their speed and direction. The ultimate goal of the project is to provide accurate and reliable
vehicle detection and tracking for use in various applications, such as traffic management,
surveillance, and autonomous driving systems.
The vehicle detection project aims to develop a system that can automatically detect and
track vehicles in real-time using computer vision and machine learning techniques. The
system employs a combination of object detection algorithms, such as YOLO, SSD, or
Faster R-CNN, and image processing techniques, such as edge detection, segmentation,
and feature extraction, to identify and track vehicles in a video stream. The system can also
perform various tasks, such as vehicle classification, counting, and speed estimation, to
provide useful information for traffic management and surveillance applications. The
project requires extensive data collection and preprocessing, as well as model training and
optimization, to achieve high accuracy and real-time performance.