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

Liang, Yongqi; Liu, Tong; Han, Tong; Ferdinando, Hany; Seppänen, Tapio; Alasaarela, Esko

doi:10.3390/rs13040628

Cited by 31 publications

(16 citation statements)

References 19 publications

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“…There are various possible strategies to identify campus violence as AI and remote monitoring capabilities advance, such as video-based techniques. Ye et al (2021) combine visual and audio data for campus violence detection. Role-playing is used for campus violence data collection, and 4096-dimension feature vectors are extracted from every 16 frames of video frames.…”

Section: Classification Of Violence Detection Techniquesmentioning

confidence: 99%

State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

Omarov

Narynov²,

Zhumanov

et al. 2022

PeerJ Computer Science

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We investigate and analyze methods to violence detection in this study to completely disassemble the present condition and anticipate the emerging trends of violence discovery research. In this systematic review, we provide a comprehensive assessment of the video violence detection problems that have been described in state-of-the-art researches. This work aims to address the problems as state-of-the-art methods in video violence detection, datasets to develop and train real-time video violence detection frameworks, discuss and identify open issues in the given problem. In this study, we analyzed 80 research papers that have been selected from 154 research papers after identification, screening, and eligibility phases. As the research sources, we used five digital libraries and three high ranked computer vision conferences that were published between 2015 and 2021. We begin by briefly introducing core idea and problems of video-based violence detection; after that, we divided current techniques into three categories based on their methodologies: conventional methods, end-to-end deep learning-based methods, and machine learning-based methods. Finally, we present public datasets for testing video based violence detectionmethods’ performance and compare their results. In addition, we summarize the open issues in violence detection in videoand evaluate its future tendencies.

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Section: Classification Of Violence Detection Techniquesmentioning

confidence: 99%

State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

Omarov

Narynov²,

Zhumanov

et al. 2022

PeerJ Computer Science

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“…Regarding the study of groups of people, advances in analysing behaviour are limited to very concrete and simple activities or actions, usually of short duration (low semantic component) such as a actions in sport games [13], [37], [42], [53], detection interactions of people inside a group [15], [57], [60], inter-group violence [51], [64], [65], among others. If we increase the number of people in the group, becoming crowds, the level of semantics is even lower, being specifically limited to tasks such as counting people and calculating crowd density [8], [18], [25], [68] or detecting movements of a mass of people or crowd collisions [21], [39], [49], [71], mainly for the purpose of security tasks.…”

Section: Related Workmentioning

confidence: 99%

Architecture for Automatic Recognition of Group Activities Using Local Motions and Context

et al. 2022

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Currently, the ability to automatically detect human behavior in image sequences is one of the most important challenges in the area of computer vision. Within this broad field of knowledge, the recognition of activities of people groups in public areas is receiving special attention due to its importance in many aspects including safety and security. This paper proposes a generic computer vision architecture with the ability to learn and recognize different group activities using mainly the local group's movements. Specifically, a multi-stream deep learning architecture is proposed whose two main streams correspond to a representation based on a descriptor capable of representing the trajectory information of a sequence of images as a collection of local movements that occur in specific regions of the scene. Additional information (e.g. location, time, etc.) to strengthen the classification of activities by including it as additional streams. The proposed architecture is capable of classifying in a robust way different activities of a group as well to deal with the one-class problems. Moreover, the use of a simple descriptor that transforms a sequence of color images into a sequence of two-image streams can reduce the curse of dimensionality using a deep learning approach. The generic deep learning architecture has been evaluated with different datasets outperforming the state-of-the-art approaches providing an efficient architecture for single and multi-class classification problems.

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“…With the increasing scale of networking and data, data is increasingly centralized, and network data security issues are becoming more prominent. The continuous advancement of the smart campus has led to an increasing number of application scenarios for the sharing of students' private data and an increasing risk of data leakage [5][6]. In addition to the construction of traditional network security devices or systems, a corresponding security system must be established in the process of data management and operation [7][8].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Smart Campus Cybersecurity and Student Privacy Protection Paths Based on Markov Models

Jianhua

2023

Applied Mathematics and Nonlinear Sciences

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This paper starts with the application of hyper-convergence technology, builds the framework of a university smart campus based on it, and gives the framework description of the smart campus. In order to analyze the network security for the smart campus, the Markov model is used as the basis combined with the reinforced Q learning algorithm for network node security detection, and a specific simulation analysis is given. The encryption performance and defense performance of the elliptic curve cryptosystem are analyzed through the elliptic curve cryptosystem to formulate the encryption scheme for students’ private data in the smart campus. The results indicate that the Markov model node detection combined with reinforcement Q-learning in this paper takes a maximum time of about 5.75s when the network node size reaches 150. When the number of nodes in the smart campus network is 30, under brute force attack, the whole network is captured only when the number of malicious nodes increases to more than 22, while under random attack, it takes as many as 30 malicious nodes to join before the network completely falls. This illustrates that the use of the Markov model can be realized to analyze the network security of the smart campus and that student privacy protection needs to further improve the awareness of student data privacy protection and develop the habit of assessing the privacy risk beforehand in their daily network behavior.

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

Cited by 31 publications

References 19 publications

State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

State-of-the-art violence detection techniques in video surveillance security systems: a systematic review

Architecture for Automatic Recognition of Group Activities Using Local Motions and Context

Optimization of Smart Campus Cybersecurity and Student Privacy Protection Paths Based on Markov Models

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