scholarly journals Deep Learning and Mel-spectrograms for Physica Violence Detection in Audio

2021 ◽  
Author(s):  
Tiago B. Lacerda ◽  
Péricles Miranda ◽  
André Câmara ◽  
Ana Paula C. Furtado
Keyword(s):  
Deep Learning ◽  
Violence Detection

Há um crescente interesse em sistemas de detecção de violência de forma automática por meio do áudio ambiente. Neste trabalho, construímos e avaliamos 4 classificadores com essa proposta. Porém, em vez de processar diretamente os sinais de áudio, nós os convertemos para imagens, conhecidas como mel-spectrograms, e em seguida utilizamos Redes Neurais Convolucionais (CNN) para tratar como um problema de classificação de imagens utilizando-se de redes pre-treinadas neste contexto. Testou-se as arquiteturas Inception v3, VGG-16, MobileNet v2 e ResNet152 v2, tendo o classificador oriundo da arquitetura MobileNet obtido os melhores resultados de classificação, quando avaliado no HEAR Dataset, criado para a realização desta pesquisa.

Violence recognition using convolutional neural network: A survey

2020 ◽  
Vol 39 (5) ◽  
pp. 7931-7952
Author(s):  
Gaurav Tripathi ◽  
Kuldeep Singh ◽  
Dinesh Kumar Vishwakarma
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Law And Order ◽  
Linear Features ◽  
Automatic Feature Extraction ◽  
Learning Platform ◽  
Violence Detection ◽  
The World ◽  
Crowd Behaviour ◽  
Behaviour Changes

Violence detection is a challenging task in the computer vision domain. Violence detection framework depends upon the detection of crowd behaviour changes. Violence erupts due to disagreement of an idea, injustice or severe disagreement. The aim of any country is to maintain law and order and peace in the area. Violence detection thus becomes an important task for authorities to maintain peace. Traditional methods have existed for violence detection which are heavily dependent upon hand crafted features. The world is now transitioning in to Artificial Intelligence based techniques. Automatic feature extraction and its classification from images and videos is the new norm in surveillance domain. Deep learning platform has provided us the platter on which non-linear features can be extracted, self-learnt and classified as per the appropriate tool. One such tool is the Convolutional Neural Networks, also known as ConvNets, which has the ability to automatically extract features and classify them in to their respective domain. Till date there is no survey of deciphering violence behaviour techniques using ConvNets. We hope that this survey becomes an exclusive baseline for future violence detection and analysis in the deep learning domain.

scholarly journals Deep Learning Approach for Violence Detection in Urban Areas

2019 ◽  
Vol 29 ◽  
pp. 03009
Author(s):  
Marius Baba ◽  
Vasile Gui ◽  
Dan Pescaru
Keyword(s):  
Deep Learning ◽  
Urban Areas ◽  
Cost Effective ◽  
Effective Solution ◽  
Prior Learning ◽  
Public Meetings ◽  
Violence Detection ◽  
Modern Cities ◽  
Sport Events ◽  
Rapid Deployment

Today modern cities tend to grow rapidly. The increased population density brings new challenges in term of public safety. Crime and violence are hard to be detected and managed especially in specific crowd environments like music concerts, sport events or public meetings. To overcome this issue the city administration should implement monitoring systems capable of detecting and analysing such situations. The work presented here combines two approaches that enable implementation of an efficient solution adapted for this purpose. The first one involves sensor networks that prove to be cost effective solution in a smart city environment. They can benefit on the existing surveillance infrastructure and allows rapid deployment. The second approach uses deep learning techniques. They demonstrate outstanding performances in image and actions classification based on a prior learning process. By combining these two approaches we succeed to obtain a real-time and cost-effective solution designed for urban area surveillance networks.

Fast Learning Through Deep Multi-Net CNN Model For Violence Recognition In Video Surveillance

2020 ◽  
Author(s):  
Aqib Mumtaz ◽  
Allah Bux Sargano ◽  
Zulfiqar Habib
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Research Work ◽  
Superior Performance ◽  
Data Sets ◽  
Camera Motion ◽  
Learning Capability ◽  
Effective Learning ◽  
Fast Learning ◽  
Violence Detection

Abstract The violence detection is mostly achieved through handcrafted feature descriptors, while some researchers have also employed deep learning-based representation models for violent activity recognition. Deep learning-based models have achieved encouraging results for fight activity recognition on benchmark data sets such as hockey and movies. However, these models have limitations in learning discriminating features for violence activity classification with abrupt camera motion. This research work investigated deep representation models using transfer learning for handling the issue of abrupt camera motion. Consequently, a novel deep multi-net (DMN) architecture based on AlexNet and GoogleNet is proposed for violence detection in videos. AlexNet and GoogleNet are top-ranked pre-trained models for image classification with distinct pre-learnt potential features. The fusion of these models can yield superior performance. The proposed DMN unleashed the integrated potential by concurrently coalescing both networks. The results confirmed that DMN outperformed state-of-the-art methods by learning finest discriminating features and achieved 99.82% and 100% accuracy on hockey and movies data sets, respectively. Moreover, DMN has faster learning capability i.e. 1.33 and 2.28 times faster than AlexNet and GoogleNet, which makes it an effective learning architecture on images and videos.

scholarly journals Smart Surveillance System using Deep Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 1151-1155
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Research Area ◽  
Violence Detection ◽  
Detection And Identification ◽  
Big Data Applications ◽  
Equal Importance ◽  
Smart Surveillance ◽  
Smart Surveillance System ◽  
Media Data

In industry and research area big data applications are consuming most of the spaces. Among some examples of big data, the video streams from CCTV cameras as equal importance with other sources like medical data, social media data. Based on the security purpose CCTV cameras are implemented in all places where security having much importance. Security can be defined in different ways like theft identification, violence detection etc. In most of the highly secured areas security plays a major role in a real time environment. This paper discusses the detecting and recognising the facial features of the persons using deep learning concepts. This paper includes deep learning concepts starts from object detection, action detection and identification. The issues recognized in existing methods are identified and summarized.

Violence detection and face recognition based on deep learning

2021 ◽  
Vol 142 ◽  
pp. 20-24
Author(s):  
Pin Wang ◽  
Peng Wang ◽  
En Fan
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Violence Detection

Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM

2019 ◽  
Vol 151 ◽  
pp. 191-200 ◽  
Author(s):  
Dinesh Jackson Samuel R. ◽  
Fenil E ◽  
Gunasekaran Manogaran ◽  
Vivekananda G.N ◽  
Thanjaivadivel T ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Real Time ◽  
Big Data Analysis ◽  
Violence Detection ◽  
Bidirectional Lstm ◽  
Football Stadium

scholarly journals Deep learning for automatic violence detection: tests on the AIRTLab dataset

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Paolo Sernani ◽  
Nicola Falcionelli ◽  
Selene Tomassini ◽  
Paolo Contardo ◽  
Aldo Franco Dragoni
Keyword(s):  
Deep Learning ◽  
Violence Detection

Deep learning based intelligent violence detection surveillance system

2021 ◽  
pp. 29-33
Author(s):  
Aaditya Kumar ◽  
Abhijeet Anand ◽  
Aniket Tomar ◽  
Piyush Yadav ◽  
Krishna Kant Singh
Keyword(s):  
Deep Learning ◽  
Surveillance System ◽  
Violence Detection
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