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Who's a Good Boy? Reinforcing Canine Behavior using Machine Learning in Real-Time

2021-01-07 05:38:09
Jason Stock, Tom Cavey
arXiv_CV
             
Abstract

In this paper we outline the development methodology for an automatic dog treat dispenser which combines machine learning and embedded hardware to identify and reward dog behaviors in real-time. Using machine learning techniques for training an image classification model we identify three behaviors of our canine companions: "sit", "stand", and "lie down" with up to 92% test accuracy and 39 frames per second. We evaluate a variety of neural network architectures, interpretability methods, model quantization and optimization techniques to develop a model specifically for an NVIDIA Jetson Nano. We detect the aforementioned behaviors in real-time and reinforce positive actions by making inference on the Jetson Nano and transmitting a signal to a servo motor to release rewards from a treat delivery apparatus.

Abstract (translated)

URL

https://arxiv.org/abs/2101.02380

PDF

https://arxiv.org/pdf/2101.02380.pdf

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