Jin, Pu und Mou, LiChao und Xia, Gui-Song und Zhu, Xiao Xiang (2022) Anomaly Detection in Aerial Videos With Transformers. IEEE Transactions on Geoscience and Remote Sensing, 60, Seite 5628213. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2022.3198130. ISSN 0196-2892.
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Offizielle URL: https://ieeexplore.ieee.org/document/9854892
Kurzfassung
Unmanned aerial vehicles (UAVs) are widely applied for purposes of inspection, search, and rescue operations by the virtue of low-cost, large-coverage, real-time, and high-resolution data acquisition capacities. Massive volumes of aerial videos are produced in these processes, in which normal events often account for an overwhelming proportion. It is extremely difficult to localize and extract abnormal events containing potentially valuable information from long video streams manually. Therefore, we are dedicated to developing anomaly detection methods to solve this issue. In this article, we create a new dataset, named Drone-Anomaly, for anomaly detection in aerial videos. This dataset provides 37 training video sequences and 22 testing video sequences from seven different realistic scenes with various anomalous events. There are 87488 color video frames (51635 for training and 35853 for testing) with the size of 640 ×640 at 30 frames/s. Based on this dataset, we evaluate existing methods and offer a benchmark for this task. Furthermore, we present a new baseline model, anomaly detection with Transformers (ANDTs), which treats consecutive video frames as a sequence of tubelets, utilizes a Transformer encoder to learn feature representations from the sequence, and leverages a decoder to predict the next frame. Our network models normality in the training phase and identifies an event with unpredictable temporal dynamics as an anomaly in the test phase. Moreover, to comprehensively evaluate the performance of our proposed method, we use not only our Drone-Anomaly dataset but also another dataset. We will make our dataset and code publicly available. A demo video is available at https://youtu.be/ancczYryOBY . We make our dataset and code publicly available ( https://gitlab.lrz.de/ai4eo/reasoning/drone-anomaly https://github.com/Jin-Pu/Drone-Anomaly ).
elib-URL des Eintrags: | https://elib.dlr.de/192685/ | ||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||
Titel: | Anomaly Detection in Aerial Videos With Transformers | ||||||||||||||||||||
Autoren: |
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Datum: | August 2022 | ||||||||||||||||||||
Erschienen in: | IEEE Transactions on Geoscience and Remote Sensing | ||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||
Band: | 60 | ||||||||||||||||||||
DOI: | 10.1109/TGRS.2022.3198130 | ||||||||||||||||||||
Seitenbereich: | Seite 5628213 | ||||||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||||||
ISSN: | 0196-2892 | ||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||
Stichwörter: | Aerial videos, anomaly detection, convolutional neural networks (CNNs), temporal reasoning, transformers, unmanned aerial vehicle (UAV) | ||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||||||
HGF - Programmthema: | Erdbeobachtung | ||||||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||||||
DLR - Forschungsgebiet: | R EO - Erdbeobachtung | ||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Künstliche Intelligenz | ||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||
Institute & Einrichtungen: | Institut für Methodik der Fernerkundung > EO Data Science | ||||||||||||||||||||
Hinterlegt von: | Haschberger, Dr.-Ing. Peter | ||||||||||||||||||||
Hinterlegt am: | 20 Dez 2022 10:59 | ||||||||||||||||||||
Letzte Änderung: | 19 Okt 2023 13:08 |
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