Project Shows How To Use Machine Learning to Detect Pedestrians


Most people are familiar with the idea that machine learning can be used to detect things like objects or people, but for anyone who’s not clear on how that process actually works should check out [Kurokesu]’s example project for detecting pedestrians. It goes into detail on exactly what software is used, how it is configured, and how to train with a dataset.

The application uses a USB camera and the back end work is done with Darknet, which is an open source framework for neural networks. Running on that framework is the YOLO (You Only Look Once) real-time object detection system. To get useful results, the system must be trained on large amounts of sample…

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Peter Bordes

Exec Chairman & Founder at oneQube
Exec Chairman & Founder of oneQube the leading audience development automation platfrom. Entrepreneur, top 100 most influential angel investors in social media who loves digital innovation, social media marketing. Adventure travel and fishing junkie.
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