Vi-Net: A Deep Violent Flow Network for Violence Detection in Video Sequences

Ehsan, Tahereh Zarrat; Mohtavipour, Seyed Mehdi

doi:10.1109/ikt51791.2020.9345617

Cited by 12 publications

(7 citation statements)

References 15 publications

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“…According to our survey, the lack of a public dataset for chicken welfare analysis in poultry houses presents a significant challenge in this field, limiting researchers' ability to work on this topic. Additionally, the number of research papers on chicken behavior analysis lags considerably behind other computer vision domain such as human behavior analysis [22][23][24] and human abnormal behavior detection [25][26][27].…”

Section: Materials and Methods 21 Datasetmentioning

confidence: 99%

DABA-Net: Deep Acceleration-Based AutoEncoder Network for Violence Detection in Surveillance Cameras

Ehsan

Nahvi

Mohtavipour

2022

2022 International Conference on Machine Vision and Image Processing (MVIP)

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Detecting anomalies in poultry houses is crucial for maintaining optimal chicken health conditions, minimizing economic losses and bolstering profitability. This paper presents a novel real-time framework for analyzing chicken behavior in cage-free poultry houses to detect abnormal behaviors. Specifically, two significant abnormalities, namely inactive broiler and huddling behavior, are investigated in this study. The proposed framework comprises three key steps: (1) chicken detection utilizing a state-of-the-art deep learning model, (2) tracking individual chickens across consecutive frames with a fast tracker module, and (3) detecting abnormal behaviors within the video stream. Experimental studies are conducted to evaluate the efficacy of the proposed algorithm in accurately assessing chicken behavior. The results illustrate that our framework provides a precise and efficient solution for real-time anomaly detection, facilitating timely interventions to maintain chicken health and enhance overall productivity on poultry farms.

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Section: Materials and Methods 21 Datasetmentioning

confidence: 99%

DABA-Net: Deep Acceleration-Based AutoEncoder Network for Violence Detection in Surveillance Cameras

Ehsan

Nahvi

Mohtavipour

2022

2022 International Conference on Machine Vision and Image Processing (MVIP)

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“…This paper [19] introduces Vi-Net, a specialised Deep Violent Flow Network designed for violence detection in video sequences, with a focus on abnormal velocity patterns. The model demonstrates excellent performance on distinct datasets: achieving 99% accuracy for movies, 94% for crowd scenarios, and 98% for hockey videos.…”

Section: Literature Reviewmentioning

confidence: 99%

Brutality detection and rendering of brutal frames

Madana Mohana,

Singh,

Kumar

et al. 2024

MATEC Web Conf.

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The popularity of anime is increasing exponentially in every part of the world due to its unique storyline, nonstop entertainment, fights, and similar type of content that can hold viewers and keeps them at the edge of their seats. However, with the increase of popularity in anime there has also been an exponential increase in violence and brutality in anime videos. Violent scenes have become much more common in anime videos when compared to generic cinema. This survey paper presents a comprehensive view on the detection of violence in movies and different scenarios using various techniques. Most commonly to automate detection of violence, machine learning is used for training the machine to detect violence. Convolution neural networks (CNN) are used very commonly to understand image pattern recognition with high accuracy. Moreover, use of other different methods such as LSTM and Markov models are also used to detect violence. The main goals kept in mind while working is to detect violence with high accuracy and to use less computation or to perform the action at a high-speed rate.

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“…Thus, it has been possible to observe the most used algorithms in phases 1 and 2 of the process of detecting violence in videos, as well as the most used combinations. [29,35,39,43,45,49,50,53,[56][57][58][59][60][62][63][64]66,71,74,76,77,[86][87][88]92].…”

Section: Violence Detection Algorithmsmentioning

confidence: 99%

Literature Review of Deep-Learning-Based Detection of Violence in Video

Negre,

Alonso,

González-Briones

et al. 2024

Sensors

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Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.

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Vi-Net: A Deep Violent Flow Network for Violence Detection in Video Sequences

Cited by 12 publications

References 15 publications

DABA-Net: Deep Acceleration-Based AutoEncoder Network for Violence Detection in Surveillance Cameras

DABA-Net: Deep Acceleration-Based AutoEncoder Network for Violence Detection in Surveillance Cameras

Brutality detection and rendering of brutal frames

Literature Review of Deep-Learning-Based Detection of Violence in Video

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