Challenges and Methods of Violence Detection in Surveillance Video: A Survey

2019 ◽  
pp. 62-73 ◽  
Author(s):  
Wafa Lejmi ◽  
Anouar Ben Khalifa ◽  
Mohamed Ali Mahjoub
Keyword(s):  
Surveillance Video ◽  
Violence Detection

scholarly journals Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

2021 ◽  
Vol 13 (4) ◽  
pp. 628
Author(s):  
Liang Ye ◽  
Tong Liu ◽  
Tian Han ◽  
Hany Ferdinando ◽  
Tapio Seppänen ◽  
...  
Keyword(s):  
Neural Network ◽  
Recognition Accuracy ◽  
Role Playing ◽  
School Bullying ◽  
Image Features ◽  
Campus Violence ◽  
Surveillance Video ◽  
Acoustic Features ◽  
Mel Frequency Cepstral Coefficients ◽  
Violence Detection

Campus violence is a common social phenomenon all over the world, and is the most harmful type of school bullying events. As artificial intelligence and remote sensing techniques develop, there are several possible methods to detect campus violence, e.g., movement sensor-based methods and video sequence-based methods. Sensors and surveillance cameras are used to detect campus violence. In this paper, the authors use image features and acoustic features for campus violence detection. Campus violence data are gathered by role-playing, and 4096-dimension feature vectors are extracted from every 16 frames of video images. The C3D (Convolutional 3D) neural network is used for feature extraction and classification, and an average recognition accuracy of 92.00% is achieved. Mel-frequency cepstral coefficients (MFCCs) are extracted as acoustic features, and three speech emotion databases are involved. The C3D neural network is used for classification, and the average recognition accuracies are 88.33%, 95.00%, and 91.67%, respectively. To solve the problem of evidence conflict, the authors propose an improved Dempster–Shafer (D–S) algorithm. Compared with existing D–S theory, the improved algorithm increases the recognition accuracy by 10.79%, and the recognition accuracy can ultimately reach 97.00%.

scholarly journals Violence detection in surveillance video using low-level features

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0203668 ◽  
Author(s):  
Peipei Zhou ◽  
Qinghai Ding ◽  
Haibo Luo ◽  
Xinglin Hou
Keyword(s):  
Surveillance Video ◽  
Low Level ◽  
Violence Detection

scholarly journals Violence Detection Using Spatiotemporal Features with 3D Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2472 ◽  
Author(s):  
Fath U Min Ullah ◽  
Amin Ullah ◽  
Khan Muhammad ◽  
Ijaz Ul Haq ◽  
Sung Wook Baik
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Smart Cities ◽  
Automatic Monitoring ◽  
Video Stream ◽  
Surveillance Video ◽  
Softmax Classifier ◽  
Spatiotemporal Features ◽  
Violence Detection ◽  
3D Cnn

The worldwide utilization of surveillance cameras in smart cities has enabled researchers to analyze a gigantic volume of data to ensure automatic monitoring. An enhanced security system in smart cities, schools, hospitals, and other surveillance domains is mandatory for the detection of violent or abnormal activities to avoid any casualties which could cause social, economic, and ecological damages. Automatic detection of violence for quick actions is very significant and can efficiently assist the concerned departments. In this paper, we propose a triple-staged end-to-end deep learning violence detection framework. First, persons are detected in the surveillance video stream using a light-weight convolutional neural network (CNN) model to reduce and overcome the voluminous processing of useless frames. Second, a sequence of 16 frames with detected persons is passed to 3D CNN, where the spatiotemporal features of these sequences are extracted and fed to the Softmax classifier. Furthermore, we optimized the 3D CNN model using an open visual inference and neural networks optimization toolkit developed by Intel, which converts the trained model into intermediate representation and adjusts it for optimal execution at the end platform for the final prediction of violent activity. After detection of a violent activity, an alert is transmitted to the nearest police station or security department to take prompt preventive actions. We found that our proposed method outperforms the existing state-of-the-art methods for different benchmark datasets.

Multiprocessor transport surveillance video system based on "system on chip" technology

2020 ◽  
Author(s):  
Ш.С. Фахми ◽  
Н.В. Шаталова ◽  
В.В. Вислогузов ◽  
Е.В. Костикова
Keyword(s):  
System On Chip ◽  
Reference Points ◽  
Surveillance Video ◽  
Video Information ◽  
Chip Technology ◽  
Intelligent Video Surveillance System ◽  
Speed Up ◽  
Algorithmic Analysis ◽  
Computational Procedures ◽  
On Chip

В данной работе предлагаются математический аппарат и архитектура многопроцессорной транспортной системы на кристалле (МПТСнК). Выполнена программно-аппаратная реализация интеллектуальной системы видеонаблюдения на базе технологии «система на кристалле» и с использованием аппаратного ускорителя известного метода формирования опорных векторов. Архитектура включает в себя сложно-функциональные блоки анализа видеоинформации на базе параллельных алгоритмов нахождения опорных точек изображений и множества элементарных процессоров для выполнения сложных вычислительных процедур алгоритмов анализа с использованием средств проектирования на базе реконфигурируемой системы на кристалле, позволяющей оценить количество аппаратных ресурсов. Предлагаемая архитектура МПТСнК позволяет ускорить обработку и анализ видеоинформации при решении задач обнаружения и распознавания чрезвычайных ситуаций и подозрительных поведений. In this paper, we propose the mathematical apparatus and architecture of a multiprocessor transport system on a chip (MPTSoC). Software and hardware implementation of an intelligent video surveillance system based on the "system on chip" technology and using a hardware accelerator of the well-known method of forming reference vectors. The architecture includes complex functional blocks for analyzing video information based on parallel algorithms for finding image reference points and a set of elementary processors for performing complex computational procedures for algorithmic analysis. using design tools based on a reconfigurable system on chip that allows you to estimate the amount of hardware resources. The proposed MPTSoC architecture makes it possible to speed up the processing and analysis of video information when solving problems of detecting and recognizing emergencies and suspicious behaviors

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