An accurate violence detection framework using unsupervised spatial–temporal action translation network

Ehsan, Tahereh Zarrat; Nahvi, Manoochehr; Mohtavipour, Seyed Mehdi

doi:10.1007/s00371-023-02865-3

Cited by 7 publications

(4 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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)

View full text Add to dashboard Cite

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.

show abstract

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)

View full text Add to dashboard Cite

show abstract

“…Transformer : Articles that apply transformer-based architectures for detecting violence in videos [ 45 , 46 ].…”

Section: Selected Article Descriptionmentioning

confidence: 99%

“…Ehsan et al [ 46 ] approached the extraction of feature frames by detecting the people present in the video and removing all background information using YOLO. The Farneback method was used for image optical flow calculation.…”

Section: Selected Article Descriptionmentioning

confidence: 99%

“…Finally, the methods in the object detection category. Several articles in this category used YOLO [ 46 , 68 , 70 , 78 , 105 ], which is a widely used object detection algorithm in images and videos that uses a convolutional neural network to identify and locate objects in real time. Rachna et al [ 105 ] and Freire et al [ 74 ] also did this in conjunction with DeepSort, which is a video object tracking algorithm.…”

Section: Description Of the Analysis Of The Selected Itemsmentioning

confidence: 99%

See 1 more Smart Citation

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

Negre,

Alonso,

González-Briones

et al. 2024

Sensors

View full text Add to dashboard Cite

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.

show abstract

Spatial Attention Transformer Based Framework for Anomaly Classification in Image Sequences

Garg,

Nigam,

Singh

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

An accurate violence detection framework using unsupervised spatial–temporal action translation network

Cited by 7 publications

References 44 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

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

Spatial Attention Transformer Based Framework for Anomaly Classification in Image Sequences

Contact Info

Product

Resources

About