Speed and Distance Detection for Highway Traffic using Python
This Python -based scheme presents an innovative approach to bolstering road safety by harnessing the power of computer vision and machine learning. The primary objective is to provide drivers with crucial real-time information for informed lane change decisions, thereby reducing the risk of accidents. The system integrates advanced techniques such as object detection, tracking algorithms, and depth estimation to analyze video feeds from highway cameras. The system's core functionality involves estimating the speed and distance of vehicles on the highway. Object detection algorithms, including YOLO, SSD, and Faster R-CNN, are employed to identify and track vehicles in the video feed. Depth estimation techniques enhance accuracy by determining relative distances between vehicles. Machine learning algorithms, such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), predict future positions and speeds, enabling the system to identify potential conflicts.
