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Open AccessBenchmark

A Controlled Benchmark of Video Violence Detection Techniques

Computer Science Department, Università degli studi di Bari “Aldo Moro”, 70121 Bari, Italy
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Information 2020, 11(6), 321; https://doi.org/10.3390/info11060321
Received: 12 May 2020 / Revised: 1 June 2020 / Accepted: 5 June 2020 / Published: 13 June 2020
This benchmarking study aims to examine and discuss the current state-of-the-art techniques for in-video violence detection, and also provide benchmarking results as a reference for the future accuracy baseline of violence detection systems. In this paper, the authors review 11 techniques for in-video violence detection. They re-implement five carefully chosen state-of-the-art techniques over three different and publicly available violence datasets, using several classifiers, all in the same conditions. The main contribution of this work is to compare feature-based violence detection techniques and modern deep-learning techniques, such as Inception V3. View Full-Text
Keywords: violence video detection; benchmark video violence; ViF; OViF; MoSIFT; IFV; ConvLSTM; Inception V3; Blobs violence video detection; benchmark video violence; ViF; OViF; MoSIFT; IFV; ConvLSTM; Inception V3; Blobs
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Convertini, N.; Dentamaro, V.; Impedovo, D.; Pirlo, G.; Sarcinella, L. A Controlled Benchmark of Video Violence Detection Techniques. Information 2020, 11, 321.

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