Human Activity Recognition Based Path Planning For Autonomous Vehicles
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
Human activity recognition (HAR) is wide research topic in a field of computer science. Improving
HAR can lead to massive breakthrough in humanoid robotics, robots used in medicine
and in the field of autonomous vehicles. The system that is able to recognise human and its
activity without any errors and anomalies, would lead to safer and more empathetic autonomous
systems. During this thesis multiple neural networks models, with different complexity,
are being investigated. Each model is re-trained on the proposed unique data set, gathered on
automated guided vehicle (AGV) with the latest and the modest sensors used commonly on
autonomous vehicles. The best model is picked out based on the final accuracy for action recognition.
Best models pipeline is fused with YOLOv3, to enhance the human detection. In
addition to pipeline improvement, multiple action direction estimation methods are proposed.
The action estimation of the human is very important aspect for self-driving car collision free
path planning.
Description
Keywords
tehisnärvivõrk, isesõitev auto, objekti tuvastus, inimese tuvastus, inimese tegevuse tuvastus, trajektoori planeerimine, Neural Networks, self-driving car, object detection, human detection, human action detection, path planning