Difference between revisions of "2018SpringTeam2"

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== Initial Research ==
== Initial Research ==


OpenCV: We needed to be able to identify a person in a live feed, and OpenCV is a library of functions specified for real-time computer vision. OpenCV is capable of identifying color blocks and boundaries, and has functions to determine centroids. The centroids would be used to identify the location of the person/object.  
The first objective was to be able to identify a person in a live feed.  


Tiny YOLO:
OpenCV: a library of functions specified for real-time computer vision. OpenCV is capable of identifying color blocks and boundaries, and has functions to determine centroids. The centroids would be used to identify the location of the person/object, and use that information to follow the person.


== Initial Research ==
Tiny YOLO: real time object detection. Tiny YOLO is capable of classification and segmentation, and can recognize objects with accuracy. Tiny YOLO has identifiable objects already built in, such as people, dogs, and cars.


OpenCV
Our project takes Tiny YOLO's person identification information and puts it into OpenCV, which we use to follow the person.

Revision as of 21:59, 7 June 2018

Team 2: Follow Me

Project Objective: Identify and follow a person, given two people in the field of view. Tiny YOLO was fed into OpenCV to identify targets in a live feed.

Some applications of this project include medical robotics (e.g. medical assistance) and military applications (e.g. target location). This project be applied to crowds, collecting data on people through human computer interactions.


Initial Research

The first objective was to be able to identify a person in a live feed.

OpenCV: a library of functions specified for real-time computer vision. OpenCV is capable of identifying color blocks and boundaries, and has functions to determine centroids. The centroids would be used to identify the location of the person/object, and use that information to follow the person.

Tiny YOLO: real time object detection. Tiny YOLO is capable of classification and segmentation, and can recognize objects with accuracy. Tiny YOLO has identifiable objects already built in, such as people, dogs, and cars.

Our project takes Tiny YOLO's person identification information and puts it into OpenCV, which we use to follow the person.