Aarhus Universitets segl

UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

Anomaly detection is a key goal of autonomous surveillance systems that should be able to alert unusual observations. In this paper, we propose a holistic anomaly detection system using deep neural networks for surveillance of critical infrastructures (e.g., airports, harbors, warehouses) using an unmanned aerial vehicle (UAV). First, we present a heuristic method for the explicit representation of spatial layouts of objects in bird-view images. Then, we propose a deep neural network architecture for unsupervised anomaly detection (UAV-AdNet), which is trained on environment representations and GPS labels of bird-view images jointly. Unlike studies in the literature, we combine GPS and image data to predict abnormal observations. We evaluate our model against several baselines on our aerial surveillance dataset and show that it performs better in scene reconstruction and several anomaly detection tasks. The codes, trained models, dataset, and video will be available at https://bozcani.github.io/uavadnet.

Titel2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
Antal sider7
Udgivelsesårokt. 2020
ISBN (Elektronisk)9781728162126
StatusUdgivet - okt. 2020
Begivenhed2020 IEEE/RSJ International Conference on Intelligent Robots and Systems - Caesars Forum, Las Vegas, USA
Varighed: 25 okt. 202024 jan. 2021


Konference2020 IEEE/RSJ International Conference on Intelligent Robots and Systems
LokationCaesars Forum
ByLas Vegas

Se relationer på Aarhus Universitet Citationsformater

ID: 215325658