A real time crime scene intelligent video surveillance systems in violence detection framework using deep learning techniques

Sahay, Kishan Bhushan; Balachander, Bhuvaneswari; Jagadeesh, B.; Kumar, G. Anand; Kumar, Rajiv; Parvathy, L. Rama

doi:10.1016/j.compeleceng.2022.108319

Cited by 30 publications

(6 citation statements)

References 19 publications

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“…These research articles including multiple other studies [38], [48] highlight that deep learning algorithms, including CNN and RNN, have successfully applied to various data types for crime prediction and classification. These studies demonstrate the versatility of deep learning algorithms in this field and provide valuable insights into the factors contributing to criminal activity.…”

Section: ) Deep Learning Based Classification Methods For Crime Predi...mentioning

confidence: 99%

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

et al. 2023

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Predicting crime using machine learning and deep learning techniques has gained considerable attention from researchers in recent years, focusing on identifying patterns and trends in crime occurrences. This review paper examines over 150 articles to explore the various machine learning and deep learning algorithms applied to predict crime. The study provides access to the datasets used for crime prediction by researchers and analyzes prominent approaches applied in machine learning and deep learning algorithms to predict crime, offering insights into different trends and factors related to criminal activities. Additionally, the paper highlights potential gaps and future directions that can enhance the accuracy of crime prediction. Finally, the comprehensive overview of research discussed in this paper on crime prediction using machine learning and deep learning approaches serves as a valuable reference for researchers in this field. By gaining a deeper understanding of crime prediction techniques, law enforcement agencies can develop strategies to prevent and respond to criminal activities more effectively.

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Section: ) Deep Learning Based Classification Methods For Crime Predi...mentioning

confidence: 99%

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

et al. 2023

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“…Using deep learning architectures, Sahay et al [17] projected a new method for real-time violence identification in crime scene videos. The determination of this study is to gather real-time crime scene footage from a shadowing scheme and extract features-based classification method.…”

Section: Related Workmentioning

confidence: 99%

Normalized Attention Neural Network with Adaptive Feature Recalibration for Detecting the Unusual Activities Using Video Surveillance Camera

Damera¹,

Vatambeti²,

Mekala³

et al. 2023

IJSSE

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Over the past few years, surveillance cameras have become common in many homes and businesses. Many businesses still employ a human monitor of their cameras, despite the fact that this individual is more probable to miss some anomalous occurrences in the video feeds owing to the inherent limitations of human perception. Numerous scholars have investigated surveillance data and offered several strategies for automatically identifying anomalous occurrences. Therefore, it is important to build a model for identifying unusual occurrences in the live stream from the security cameras. Recognizing potentially dangerous situations automatically so that appropriate action may be taken is crucial and can be of great assistance to law enforcement. In this research work, starting with an MRCNN for feature extraction and AFR for fine-tuning, this architecture has a number of key components (AFR). To increase the quality of the features extracted by the MRCNN, the AFR replicas the inter-dependencies among the features to enhance the quality of the low-and high-frequency features extracted. Then, a normalized attention network (NAN) is used to learn the relationships between channels, which used to identify the violence and speeds up the convergence process for training a perfect. Furthermore, the dataset took real-time security camera feeds from a variety of subjects and situations, as opposed to the hand-crafted datasets utilized in prior efforts. We also demonstrate the method's capability of assigning the correct category to each anomaly by classifying normal and abnormal occurrences. The method divided the information gathered into three primary groups: those in need of fire protection, those experiencing theft or violence, and everyone else. The study applied the proposed approach to the UCF-Crime dataset, where it outperformed other models on the same dataset.

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“…In particular, it is about: a) the use of neural networks for the analysis of video data, shoe prints from the crime scene (Sahay et al, 2022;Wen et al, 2023); b) 3D-visualization, which reveals the connection between the evidence and allows to fully reconstruct the scene (Wang et al, 2019;Cunha et al, 2022;Ospina-Bohorquez´et al, 2023), as well as provide experts with remote access to it (Yu et al, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

The Use of Databases in the Analysis of the Scene of Crime

Akhtyrska,

Kostiuchenko,

Sereda

et al. 2023

RDLUZ

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The problems of using databases in the crime scene analysis are due to changes in the structure of crime, adaptation of innovative technologies to the needs of law enforcement, and protection of human rights. The complex of these aspects determines the relevance of the topic. The aim of the study is identifying the peculiarities of using databases in the crime scene analysis and the prospects for improving law enforcement activities with a view to crime trends. The research employed logical, comparative methods, forecasting. It was revealed that the mechanism of database creation and use is aimed at the fulfilment of the tasks of working with traces at the crime scene. Correspondence of databases to the needs of law enforcement activities is assessed through criteria grouped into resource, organizational and regulatory clusters. Prospective databases should take crime trends into account. The latest methods of working with traces are the final stage of the adaptation of research in the field of criminal justice. The academic novelty of the study consists in a critical examination of the use of databases in the crime scene analysis as a complex of the issues of communications and innovations in law enforcement activities. The study opens up prospects for the development of unified algorithms for information exchange for counteraction to transnational crime.

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A real time crime scene intelligent video surveillance systems in violence detection framework using deep learning techniques

Cited by 30 publications

References 19 publications

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions

Normalized Attention Neural Network with Adaptive Feature Recalibration for Detecting the Unusual Activities Using Video Surveillance Camera

The Use of Databases in the Analysis of the Scene of Crime

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