This article series will show you how to build a reasonably accurate traffic speed detector using nothing but Deep Learning, and run it on an edge device like a Raspberry Pi.
In this article, we set up a development environment on Windows 10 for cross-platform computer vision and machine learning projects to run on our Pi device.
In this article, we have a look at the details of the TrafficCV implementation and the various object detection models to use for detecting vehicles and calculating their speed.
In this article, we focus on developing a computer vision framework that can run the various Machine Learning and neural network models – like SSD MobileNet – on live and recorded vehicle traffic videos.
In this article, we explore the different ways of measuring vehicle speed and the different Deep Learning models for object detection that can be used in our TrafficCV program.
In this we discuss improvements we can make to the software in terms of performance or accuracy. We also compare our homebrew open-source system to commercial vehicle speed detection systems.
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