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Reading images from a webcam and monitoring traffic using a Haar classifier to detect cars.
License: MIT License
This project forked from ckuhn517/cvtrafficcam
Reading images from a webcam and monitoring traffic using a Haar classifier to detect cars.
License: MIT License
-------------------------------------- Computer Vision Traffic Camera Project -------------------------------------- Author: Chris Kuhn Location: https://github.com/cKuhn517/cvTrafficCam.git This is a simple project for detecting vehicles in a video stream and recording traffic and parking conditions during the day. This information will be used to predict times of heavy traffic and the availability of street parking. The program will be made as lightweight as possible so a simple server or raspberry pi can be used to monitor a continuous video stream from a webcam or ip camera. This project is for my own experimentation and education and is free for others to use and modify as they see fit. For more information see LICENSE. --------------------------------- Requirements --------------------------------- - Python 2.7 - OpenCV 3.1 --------------------------------- Features --------------------------------- This is a list of current and planned features for this project. --------------------- Current Features --------------------- - Haar cascade training (follow instructions in buildHaarClassifier.py) - Vehicle detection (haarTest.py) --------------------- Planned Features --------------------- - Motion detection triggering for increased performance - Automatic reduction in framerate if necessary to keep up with live feed - Automatic traffic lane detection - Automatic parking zone detection - Storage of traffic and parking data - Traffic prediction - Parking availability prediction --------------------------------- Change Log --------------------------------- - v0.1 -- Implemented and trained Haar cascade for vehicle detection
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