• Title/Summary/Keyword: Violence Detection

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Extraction of Assault and Violence-in Elevator (폭행 및 폭력의 추출-엘리베이터 내에서)

  • Shin, Seong-Yoon;Lee, Hyun-Chang
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2013.01a
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    • pp.95-97
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    • 2013
  • 현재 엘리베이터 내에서는 수많은 범죄들이 발생하고 있으며, 그 대담성 또한 날로 증가하고 있다. 본 논문에서는 불법한 유형력의 행사인 폭행과 이러한 폭행에 동반되는 물리적인 행사인 폭력에 대하여 의미를 알아본다. 그리고 엘리베이터 내에서 발생하는 폭행과 폭력을 추출하는 방법을 제시하도록 한다. 장면 전환 검출 방법 중의 하나인 컬러히스토그램 기법을 사용하여 키프레임을 추출한다. 추출된 키 프레임들은 영상 포렌식에서 범죄 현장을 담은 장면의 증거자료인 프레임이 된다.

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A Study on Assault and Violence in Elevator (엘리베이터 내에서 폭행 및 폭력사건에 관한 연구)

  • Shin, Seong-Yoon;Shin, Kwang-Seong;Lee, Jong-Chan;Park, Sang-Joon;Rhee, Yang-Won;Lee, Hyun-Chang
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2012.10a
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    • pp.60-62
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    • 2012
  • Assault said to contact the opponent's body with the power to superior opponent. In other words, it is the act of hitting an opponent with the fist. In this paper, the violence and assaults that occur in elevators extracted using a color histogram of scene change detection technique.

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A Study on the Game Violence Detection System Using Cloud Vision API (Cloud Vision API를 활용한 게임물 폭력성 감지 시스템에 관한 연구)

  • Jo, Hojeong;Moon, Mikyeong
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2022.07a
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    • pp.509-510
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    • 2022
  • 한 해 100만건 이상의 모바일 게임이 출시되고 있으며, 게임 수가 많은 만큼 게임물 등급 관리에 있어서 객관적이고 투명한 업무처리는 필수적이다. 그러나 현재 사업자 자체 등급분류는 오직 게임 개발사가 제출한 서류에 의존하며, 사후 모니터링을 통해 제재하는 실정이다. 이에 따라, 사전에 폭력성 유뮤를 식별할 수 있는 기술이 필요하다. 본 논문에서는 Cloud Vision API를 활용하여 프레임 수를 설정하고 해당 프레임에 대한 폭력성 정도를 수치로 출력하여 해당 영상에 문제 유무를 사전에 식별할 수 있도록 돕는 시스템에 대해 기술한다. 이 시스템을 통해 게임물관리위원회 인력과 예산 한계로 인해 계속 불거지고 있는 연 100만 건에 달하는 등급분류 처리의 정확성과 사후 모니터링에 효과가 있을 것이다.

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Deep Learning based violent protest detection system

  • Lee, Yeon-su;Kim, Hyun-chul
    • Journal of the Korea Society of Computer and Information
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    • v.24 no.3
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    • pp.87-93
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    • 2019
  • In this paper, we propose a real-time drone-based violent protest detection system. Our proposed system uses drones to detect scenes of violent protest in real-time. The important problem is that the victims and violent actions have to be manually searched in videos when the evidence has been collected. Firstly, we focused to solve the limitations of existing collecting evidence devices by using drone to collect evidence live and upload in AWS(Amazon Web Service)[1]. Secondly, we built a Deep Learning based violence detection model from the videos using Yolov3 Feature Pyramid Network for human activity recognition, in order to detect three types of violent action. The built model classifies people with possession of gun, swinging pipe, and violent activity with the accuracy of 92, 91 and 80.5% respectively. This system is expected to significantly save time and human resource of the existing collecting evidence.

A Study on a Violence Recognition System with CCTV (CCTV에서 폭력 행위 감지 시스템 연구)

  • Shim, Young-Bin;Park, Hwa-Jin
    • Journal of Digital Contents Society
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    • v.16 no.1
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    • pp.25-32
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    • 2015
  • With the increased frequency of crime such as assaults and sexual violence, the reliance on CCTV in arresting criminals has increased as well. However, CCTV, which should be monitored by human labor force at all times, has limits in terms of budget and man-power. Thereby, the interest in intelligent security system is growing nowadays. Expanding the techniques of an objects behavior recognition in previous studies, we propose a system to detect forms of violence between 2~3 objects from images obtained in CCTV. It perceives by detecting the object with the difference operation and the morphology of the background image. The determinant criteria to define violent behaviors are suggested. Moreover, provable decision metric values through measurements of the number of violent condition are derived. As a result of the experiments with the threshold values, showed more than 80% recognition success rate. A future research for abnormal behaviors recognition system in a crowded circumstance remains to be developed.

Multimodal approach for blocking obscene and violent contents (멀티미디어 유해 콘텐츠 차단을 위한 다중 기법)

  • Baek, Jin-heon;Lee, Da-kyeong;Hong, Chae-yeon;Ahn, Byeong-tae
    • Journal of Convergence for Information Technology
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    • v.7 no.6
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    • pp.113-121
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    • 2017
  • Due to the development of IT technology, harmful multimedia contents are spreading out. In addition, obscene and violent contents have a negative impact on children. Therefore, in this paper, we propose a multimodal approach for blocking obscene and violent video contents. Within this approach, there are two modules each detects obsceneness and violence. In the obsceneness module, there is a model that detects obsceneness based on adult and racy score. In the violence module, there are two models for detecting violence: one is the blood detection model using RGB region and the other is motion extraction model for observation that violent actions have larger magnitude and direction change. Through result of these three models, this approach judges whether or not the content is harmful. This can contribute to the blocking obscene and violent contents that are distributed indiscriminately.

Danger Alert Surveillance Camera Service using AI Image Recognition technology (인공지능 이미지 인식 기술을 활용한 위험 알림 CCTV 서비스)

  • Lee, Ha-Rin;Kim, Yoo-Jin;Lee, Min-Ah;Moon, Jae-Hyun
    • Annual Conference of KIPS
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    • 2020.11a
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    • pp.814-817
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    • 2020
  • The number of single-person households is increasing every year, and there are also high concerns about the crime and safety of single-person households. In particular, crimes targeting women are increasing. Although home surveillance camera applications, which are mostly used by single-person households, only provide intrusion detection functions, this service utilizes AI image recognition technologies such as face recognition and object detection to provide theft, violence, stranger and intrusion detection. Users can receive security-related notifications, relieve their anxiety, and prevent crimes through this service.

Implementation of Algorithms for Detection of Violence (폭행상황 감지를 위한 알고리즘의 구현)

  • Choi, Dong-Hwan;Yun, Sung-Yeol;Park, Seok-Cheon
    • Annual Conference of KIPS
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    • 2012.11a
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    • pp.435-437
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    • 2012
  • 최근 다양한 곳에서 활용되는 CCTV를 정부에서는 통합관리하기 위해 통합관제센터를 구축하여 운영 중에 있으며, 통합관제센터에서 제공하는 서비스 중에서 폭행이라는 복합적 상황을 감지하기 위한 알고리즘을 구현하였다. 구현된 알고리즘 테스트를 위해 통합관제 테스트 베드를 구축하였으며, 객체생성 모듈을 개발하고 객체에 대한 정보를 생성하여 테스트를 진행하였다. 테스트 결과 유동인구에 따라서 구현한 시스템의 성능을 확인하였다.

Aircraft Crime and the Damage Relief (항공 범죄와 그 피해구제)

  • Kim, Sun-Ihee;Ahn, Jin-Young
    • The Korean Journal of Air & Space Law and Policy
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    • v.24 no.1
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    • pp.3-35
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    • 2009
  • A concept of Aircraft crime includes an Air range, unlawful seizure of aircraft and unlawful acts against the safety of civil aviation. There are international treaties and conventions which have mainly been enacted by ICAO. The following treaties and conventions are categorical and unconditional norms that any States are clearly condemned. Convention on Offences and Certain other Acts Committed on Board Aircraft, Convention for the Suppression of Unlawful Seizure of Aircraft, Convention for the suppression of unlawful acts against the safety of civil aviation, Protocol for the Suppression of Unlawful Acts of Violence at Airports Serving International Civil Aviation, Convention on the Marking of Plastic Explosives for the Purpose of Detection In this essay, I present the meaning of the aircraft crime mentioned on the treaties above and jurisdiction of the crime. Moreover, I explain how to demand reparation for damages onboard or on the surface when an aircraft crime is occurred. Lastly, I indicate legal bases of how to protect the victims of the aircraft crime by mentioning specific cases relating to the crime.

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A SURVEY OF THE PSYCHOSIS AMONG SCHOOL VIOLENCE VICTIMS (학교폭력 피해자의 정신병 실태 조사)

  • Kwon, Seok-Woo;Shin, Min-Sup;Cho, Soo-Churl;Shin, Sung-Woong
    • Journal of the Korean Academy of Child and Adolescent Psychiatry
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    • v.11 no.1
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    • pp.124-143
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    • 2000
  • Objectives:The primary purpose of this study is to understand the psychopathology of the victims of school violence in terms of early psychosis. By doing this, the early detection of psychosis among the victims is possible, and early detection may lead to early intervention. Methods:Two-thousand and nine-hundred seventy two students from 16 middle schools in Seoul were asked to fill out questionnaire comprised of popularity and intellectual and school status of Piers-Harris Children's Self Concept Inventory, Symptom Check List-90-Revised, and Ostracism Scale. The subjects whose scores upon Ostracism Scale were higher than average by two standard deviation were labeled as ‘Repelled and Isolated group', and subjects whose scores on popularity were significantly lower than average and whose scores on psychoticism of SCL-90-R were higher than average were defined as 'tentative early psychosis group'. Odds ratios were calculated from the numbers of subjects with and without high psychoticism scores and high ostracism scores. On the subjects of 'tentative early psychotic group', we examined every clinical characteristic and conducted correlation analysis and regression analysis in order to find out the risk factors and to construct theoretical model that explains the psychoticism scores. Results:The results were as follows:1) Total 157(5.3%) subjects were rated significantly higher on ostracism scale, and among them, 47 subjects(29.9%) were rated significantly higher than average on psychoticism scale, while only 50 subjects among 2,135 students who were rated within normal range showed significantly higher score on psychoticism scale. Odds ratio for psychotic group of isolated group were 17.82 and it was statistically significant. 2) Forty-seven subjects(31 boys, 16 girls) who were rated as they were unpopular and rejected from peers had significantly higher psychoticism scores. They were not significantly different from simply high psychoticism subjects in anxiety, social anhedonia scale, magical thinking, obsessivecompulsive symptoms, phobic anxiety, psychoticism, somatization, but showed higher ostracism scores and paranoid tendencies. Among school violence victims, who rated themselves unpopular and showed higher psychoticism scores, the psychoticism scores were mainly explained by anxiety, depression, hostility, interpersonal sensitivity, obsessive-compulsive symptoms, paranoid tendency, somatization scales($r^2=0.93$). Conclusion:Thus, it can be concluded that the subjects with higher ostracism score have the substantially high risk for psychosis development. By these results, we propose that school violence victims with anxiety, depression, hostility, interpersonal sensitivity, obsessive-compulsive symptoms, paranoid tendency, somatization should be tested individually considering school adjustment, attentional deficit, concept formation problems.

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