scholarly journals WiVi: A Ubiquitous Violence Detection System With Commercial WiFi Devices

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 6662-6672 ◽  
Author(s):  
Lei Zhang ◽  
Xin Ruan ◽  
Ju Wang
Keyword(s):  
Detection System ◽  
Violence Detection

scholarly journals Physical Violence Detection for Preventing School Bullying

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Liang Ye ◽  
Hany Ferdinando ◽  
Tapio Seppänen ◽  
Esko Alasaarela
Keyword(s):  
Physical Violence ◽  
Promising Result ◽  
Detection System ◽  
Role Playing ◽  
School Bullying ◽  
Dropping Out ◽  
Daily Life Activities ◽  
Everyday Activities ◽  
Violence Detection ◽  
Physical Bullying

School bullying is a serious problem among teenagers, causing depression, dropping out of school, or even suicide. It is thus important to develop antibullying methods. This paper proposes a physical bullying detection method based on activity recognition. The architecture of the physical violence detection system is described, and a Fuzzy Multithreshold classifier is developed to detect physical bullying behaviour, including pushing, hitting, and shaking. Importantly, the application has the capability of distinguishing these types of behaviour from such everyday activities as running, walking, falling, or doing push-ups. To accomplish this, the method uses acceleration and gyro signals. Experimental data were gathered by role playing school bullying scenarios and by doing daily-life activities. The simulations achieved an average classification accuracy of 92%, which is a promising result for smartphone-based detection of physical bullying.

scholarly journals A fully integrated violence detection system using CNN and LSTM

2021 ◽  
Vol 11 (4) ◽  
pp. 3374
Author(s):  
Sarthak Sharma ◽  
B. Sudharsan ◽  
Saamaja Naraharisetti ◽  
Vimarsh Trehan ◽  
Kayalvizhi Jayavel
Keyword(s):  
Real Time ◽  
Detection System ◽  
Police Department ◽  
Learning Networks ◽  
Shopping Malls ◽  
Fully Integrated ◽  
Video Footage ◽  
Real Time Analysis ◽  
Violence Detection ◽  
Sports Stadiums

Recently, the number of violence-related cases in places such as remote roads, pathways, shopping malls, elevators, sports stadiums, and liquor shops, has increased drastically which are unfortunately discovered only after it’s too late. The aim is to create a complete system that can perform real-time video analysis which will help recognize the presence of any violent activities and notify the same to the concerned authority, such as the police department of the corresponding area. Using the deep learning networks CNN and LSTM along with a well-defined system architecture, we have achieved an efficient solution that can be used for real-time analysis of video footage so that the concerned authority can monitor the situation through a mobile application that can notify about an occurrence of a violent event immediately.

scholarly journals Violence detection explanation via semantic roles embeddings

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Enrico Mensa ◽  
Davide Colla ◽  
Marco Dalmasso ◽  
Marco Giustini ◽  
Carlo Mamo ◽  
...  
Keyword(s):  
Emergency Room ◽  
Language Processing ◽  
Detection System ◽  
Real Data ◽  
Hospital Staff ◽  
The Body ◽  
Hybrid Technique ◽  
Narrative Texts ◽  
Semantic Roles ◽  
Violence Detection

Abstract Background Emergency room reports pose specific challenges to natural language processing techniques. In this setting, violence episodes on women, elderly and children are often under-reported. Categorizing textual descriptions as containing violence-related injuries (V) vs. non-violence-related injuries (NV) is thus a relevant task to the ends of devising alerting mechanisms to track (and prevent) violence episodes. Methods We present ViDeS (so dubbed after Violence Detection System), a system to detect episodes of violence from narrative texts in emergency room reports. It employs a deep neural network for categorizing textual ER reports data, and complements such output by making explicit which elements corroborate the interpretation of the record as reporting about violence-related injuries. To these ends we designed a novel hybrid technique for filling semantic frames that employs distributed representations of terms herein, along with syntactic and semantic information. The system has been validated on real data annotated with two sorts of information: about the presence vs. absence of violence-related injuries, and about some semantic roles that can be interpreted as major cues for violent episodes, such as the agent that committed violence, the victim, the body district involved, etc.. The employed dataset contains over 150K records annotated with class (V,NV) information, and 200 records with finer-grained information on the aforementioned semantic roles. Results We used data coming from an Italian branch of the EU-Injury Database (EU-IDB) project, compiled by hospital staff. Categorization figures approach full precision and recall for negative cases and.97 precision and.94 recall on positive cases. As regards as the recognition of semantic roles, we recorded an accuracy varying from.28 to.90 according to the semantic roles involved. Moreover, the system allowed unveiling annotation errors committed by hospital staff. Conclusions Explaining systems’ results, so to make their output more comprehensible and convincing, is today necessary for AI systems. Our proposal is to combine distributed and symbolic (frame-like) representations as a possible answer to such pressing request for interpretability. Although presently focused on the medical domain, the proposed methodology is general and, in principle, it can be extended to further application areas and categorization tasks.

Surveillance Violence Detection System

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Siddhhesh Gathibandhe ◽  
Abhishekh Chimantrawar ◽  
Saurabh Pusdekar ◽  
Vrushabh Dhole
Keyword(s):  
Detection System ◽  
Violence Detection

scholarly journals An assault detection system based on human Pose Tracking for video surveillance

2019 ◽  
Author(s):  
Pedro Gabriel Santos do Couto Soares ◽  
Arnaldo Barros Da Silva ◽  
Luis Filipe Alves Pereira
Keyword(s):  
Video Surveillance ◽  
New Technologies ◽  
State Of The Art ◽  
Detection System ◽  
Daily Lives ◽  
Pose Tracking ◽  
Violence Detection ◽  
Human Pose Tracking ◽  
Human Pose ◽  
Accuracy Rates

The development of new technologies for video surveillance and automatic violence detection can bring more security to our daily lives. Solutions previously published in the state-of-the-art had presented techniques to detect violence at movie scenes, sports matches, or crowds. In this work, we propose a novel system architecture based on human Pose Track for detecting evidence of assaults in real-world videos from closed-circuit television (CCTV) of Brazilian lottery agencies. The results showed that our method can identify individuals with hands up and lying down with accuracy rates up to 85%. We believe that the detection of potentially risky situations in real-time is a crucial tool in the fighting against crime.

scholarly journals Violence Detection System using Convolution Neural Network

2019 ◽  
Vol 6 (2) ◽  
pp. 5-8
Author(s):  
Goutham Sakthivinayagam ◽  
Raveena Easawarakumar ◽  
Alagappan Arunachalam ◽  
Pandi M
Keyword(s):  
Neural Network ◽  
Detection System ◽  
Convolution Neural Network ◽  
Violence Detection

Analysis of electron-diffraction intensity profiles using a photodiode-array detection system

1983 ◽  
Vol 41 ◽  
pp. 322-323
Author(s):  
J. B. Warren
Keyword(s):  
Electron Diffraction ◽  
Diffraction Pattern ◽  
Detection System ◽  
High Energy ◽  
Photographic Emulsion ◽  
Diffraction Intensity ◽  
Silicon Photodiode ◽  
Photodiode Array Detection ◽  
Analog To Digital ◽  
Photodiode Array

Electron diffraction intensity profiles have been used extensively in studies of polycrystalline and amorphous thin films. In previous work, diffraction intensity profiles were quantitized either by mechanically scanning the photographic emulsion with a densitometer or by using deflection coils to scan the diffraction pattern over a stationary detector. Such methods tend to be slow, and the intensities must still be converted from analog to digital form for quantitative analysis. The Instrumentation Division at Brookhaven has designed and constructed a electron diffractometer, based on a silicon photodiode array, that overcomes these disadvantages. The instrument is compact (Fig. 1), can be used with any unmodified electron microscope, and acquires the data in a form immediately accessible by microcomputer.Major components include a RETICON 1024 element photodiode array for the de tector, an Analog Devices MAS-1202 analog digital converter and a Digital Equipment LSI 11/2 microcomputer. The photodiode array cannot detect high energy electrons without damage so an f/1.4 lens is used to focus the phosphor screen image of the diffraction pattern on to the photodiode array.

Data Acquisition and Handling Unit for a Quantitative Use of Electron Energy Loss Spectroscopy

1977 ◽  
Vol 35 ◽  
pp. 232-233 ◽  
Author(s):  
P. Trebbia ◽  
P. Ballongue ◽  
C. Colliex
Keyword(s):  
Electron Microscope ◽  
Energy Loss ◽  
Electron Energy ◽  
Electron Energy Loss Spectroscopy ◽  
Single Channel ◽  
Detection System ◽  
Surface Barrier ◽  
Solid State Detector ◽  
Electrostatic Mirror ◽  
Electron Energy Loss

An effective use of electron energy loss spectroscopy for chemical characterization of selected areas in the electron microscope can only be achieved with the development of quantitative measurements capabilities.The experimental assembly, which is sketched in Fig.l, has therefore been carried out. It comprises four main elements.The analytical transmission electron microscope is a conventional microscope fitted with a Castaing and Henry dispersive unit (magnetic prism and electrostatic mirror). Recent modifications include the improvement of the vacuum in the specimen chamber (below 10-6 torr) and the adaptation of a new electrostatic mirror.The detection system, similar to the one described by Hermann et al (1), is located in a separate chamber below the fluorescent screen which visualizes the energy loss spectrum. Variable apertures select the electrons, which have lost an energy AE within an energy window smaller than 1 eV, in front of a surface barrier solid state detector RTC BPY 52 100 S.Q. The saw tooth signal delivered by a charge sensitive preamplifier (decay time of 5.10-5 S) is amplified, shaped into a gaussian profile through an active filter and counted by a single channel analyser.

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