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Person Classification Leveraging Convolutional Neural Network for Obstacle Avoidance via Unmanned Aerial Vehicles

Junoh, S. and Aouf, N. ORCID: 0000-0001-9291-4077 (2017). Person Classification Leveraging Convolutional Neural Network for Obstacle Avoidance via Unmanned Aerial Vehicles. Paper presented at the 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS), 3-5 Oct 2017, Linkoping, Sweden.

Abstract

Obstacle avoidance capability for Unmanned Aerial Vehicles (UAVs) remains an active research in order to provide a better sense-and-avoid technology. More severely, in an environment where it contains and involves humans, the capability required is of high reliability and robustness. Prior to avoiding obstacles during mission, having a high performance of obstacle detection is deemed important. We first tackled the detection problem by solving the classification task. In this work, humans were treated as a special type of obstacles in indoor environment by which they may potentially cooperate with UAVs in indoor setting. While existing works have long been focusing on using classical computer vision techniques that suffer from substantial disadvantages with respect to robustness, studies on the use of deep learning approach i.e. Convolutional Neural Network (CNN) to achieve this purpose are still scarce. Using this approach for binary person classification task has revealed improved performance of more than 99% both for True Positive Rate (TPR) and True Negative Rate (TNR), hence, is promising for realizing robust obstacle avoidance.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
U Military Science
Departments: School of Mathematics, Computer Science & Engineering > Engineering > Electrical & Electronic Engineering
URI: https://openaccess.city.ac.uk/id/eprint/22088
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