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WVD: A New Synthetic Dataset for Video-based Violence Detection

Nadeem, Muhammad Shahroz, Franqueira, Virginia N. L., Kurugollu, Fatih, Zhai, Xiaojun (2019) WVD: A New Synthetic Dataset for Video-based Violence Detection. In: Bramer, Max and Petridis, Miltos, eds. Lecture Notes in Artificial Intelligence. Artificial Intelligence XXXVI: 39th SGAI International Conference on Artificial Intelligence, AI 2019, Cambridge, UK, December 17–19, 2019, Proceedings. 11927. pp. 158-164. Springer ISBN 978-3-030-34884-7. (doi:10.1007/978-3-030-34885-4_13) (KAR id:77170)

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Violence detection is becoming increasingly relevant in many areas such as for automatic content filtering, video surveillance and law enforcement. Existing datasets and methods discriminate between violent and non-violent scenes based on very abstract definitions of violence. Available datasets, such as "Hockey Fight" and "Movies", only contain fight versus non-fight videos; no weapons are discriminated in them. In this paper, we focus explicitly on weapon-based fighting sequences and propose a new dataset based on the popular action-adventure video game Grand Theft Auto-V (GTA-V). This new dataset is called "Weapon Violence Dataset" (WVD). The choice for a virtual dataset follows a trend which allows creating and labelling as sophisticated and large volume, yet realistic, datasets as possible. Furthermore, WVD also avoids the drawbacks of access to real data and potential implications. To the best of our knowledge no similar dataset, that captures weapon-based violence, exists. The paper evaluates the proposed dataset by utilising local feature descriptors using an SVM classifier. The extracted features are aggregated using the Bag of Visual Word (BoVW) technique to classify weapon-based violence videos. Our results indicate that SURF achieves

the best performance.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1007/978-3-030-34885-4_13
Uncontrolled keywords: Violence Detection, Dataset, Hot and Cold Weapons, Video Classifcation, GTA-V, Computer Games, WVD
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Virginia Franqueira
Date Deposited: 16 Oct 2019 15:31 UTC
Last Modified: 16 Feb 2021 14:08 UTC
Resource URI: (The current URI for this page, for reference purposes)
Nadeem, Muhammad Shahroz:
Franqueira, Virginia N. L.:
Kurugollu, Fatih:
Zhai, Xiaojun:
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