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

TL;DR

This paper presents a real-time machine learning system integrated with embedded hardware to identify and reinforce specific dog behaviors, enabling automatic treat dispensing with high accuracy and speed.

Contribution

It introduces a methodology for developing an embedded ML system for canine behavior recognition and reinforcement, optimized for NVIDIA Jetson Nano hardware.

Findings

Achieved up to 92% test accuracy in behavior classification

Operates at 39 frames per second in real-time

Successfully integrated behavior detection with treat dispensing hardware

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.