AI Player Recognition


The goal is to recognize hockey players by his uniform number (and then control the camera to follow him).
Great accuracy has been achieved in third quarter of 2017, thanks to our Chinese contractor, Zhao.

We went through two iterations, both involve OpenCV and TensorFlow.
Iteration 1
Number Bipmap Capture: OpenCV
Number Recognition: TensorFlow/Keras, CNN, MNIST dataset
Problems:
-As the uniform number is usually two digits, CNN/MNIST only takes single digit bitmap as input.
Separating the digits is challenging.
-OpenCV captures all number without considering the context/background.
However, only numbers on uniform should be captured.
In order to conquer these two problems, we moved to iteration 2.
Iteration 2
Number Bipmap Capture: Tensorflow Object Detection Api(June 2017) replaces foreground detection.
By this technology, only numbers on a human are captured, regardless he’s moving or not.
Number Recognition:
SVHN replaces MNIST so that we can send a bitmap of “12” instead of sending two bitmaps for 1 and 2.
References:
https://github.com/tensorflow/models/tree/master/research/object_detection
http://ufldl.stanford.edu/housenumbers

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