Advanced Traffic monitoring:Precise vehicle counting,speed estimation,color recognition and Type classification utilizing ROC curve analysis

The vehicle detection and counting project aims to develop a robust and efficient system to automatically detect and count vehicles in a given video stream using TensorFlow, a popular deep learning framework. The project utilizes the SSD with Mobilenet model,a state-of-the-art object detection architecture, to achieve real-time vehicle detection. The system processes each frame of the input video and identifies vehicles, providing information such as the number of vehicles detected, color of the vehicle, their movement direction, and estimated speed in lilometers per hour. The implementation involves loading a pre-trained frozen detection graph for the SSD with Mobilenet model, and then feeding each frame of the video to the model for detection. The detection results are visualized on the video frames with bounding boxes and corresponding class labels. The system also includes a region of interest (ROI) line to count the number of vehicles passing over it,thereby leeping track of the total number of vehicles passing through the scene.