Document Type

Article

Publication Date

2023

Publication Source

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Abstract

Anomaly detection plays an increasingly important role in video surveillance and is one of the issues that have attracted various communities, such as computer vision, machine learning, and data mining in recent years. Moreover, drones equipped with cameras have quickly been deployed to a wide range of applications, starting from border security applications to street monitoring systems. However, there is a notable lack of adequate drone-based datasets available to detect unusual events in the urban traffic environment, especially in roundabouts, due to the density of interaction between road users and vehicles. To promote the development of anomalous event detection with drones in the complex traffic environment, we construct a novel large-scale drone dataset to detect anomalies involving realistic roundabouts in Vietnam, covering a large variety of anomalous events. Traffic at a total of three different roundabouts in Ho Chi Minh City was recorded with a camera-equipped drone. The resulting dataset contains 51 videos with total data traffic of nearly 6.5 h, captured across 206K frames with ten abnormal event types. Based on this dataset, we comprehensively evaluate the current state-of-the-art algorithms and what anomaly detection can do in drone-based video surveillance. This study presents a detailed description of the proposed UIT-ADrone dataset, along with information regarding data distribution, protocols for evaluation, baseline experimental results on our dataset, and other benchmark datasets, discussions, and paves the way for future work.

Inclusive pages

5590-5601

ISBN/ISSN

1939-1404

Document Version

Published Version

Comments

This open-access article is provided for download in compliance with the publisher’s policy on self-archiving. To view the version of record, use the DOI: https://doi.org/10.1109/JSTARS.2023.3285905

Publisher

IEEE-INST Electrical Electronics Engineers INC

Volume

16

Peer Reviewed

yes

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