Intelligent Video Surveillance Based on YOLO: A Comparative Study

Garg, Rakesh; Singh, Shiv

doi:10.1109/icac353642.2021.9697321

Cited by 10 publications

(5 citation statements)

References 13 publications

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“…Our findings support the notion proposed by [19] that employing a CNN-BiLSTM architecture yields improved accuracy. This architecture, which processes temporal features bidirectionally through the combination of convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) layers, leverages the advantages of capturing temporal dependencies in both forward and backward directions.…”

Section: Resultssupporting

confidence: 91%

Real time Violence and Weapon detection and Alert System

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Patil,

Pattanshetti

2024

Preprint

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With the proliferation of surveillance systems and the increasing need for public safety, real-time violence detection has emerged as a crucial research area. This project presents a novel approach to real-time violence detection leveraging the power of deep learning through the integration of Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory Networks (LSTM) and weapon detection using You Only Look Once algorithm. The proposed system not only accurately identifies violent activities and weapons in video streams but also promptly issues alerts for timely intervention. The proposed system aims to address the challenges associated with accurately and swiftly identifying violent activities and weapons in video streams, making it applicable to diverse settings such as public spaces. The CNN component of the model is designed to automatically extract relevant spatial features from video frames, enabling the network to discern patterns associated with violent actions. Simultaneously, the Bi-LSTM network is employed to capture temporal dependencies and dynamic sequences within the video stream, allowing the model to understand the evolution of events over time. The fusion of spatial and temporal information enhances the robustness and accuracy of violence detection, especially in complex scenarios where contextual information is crucial. To further augment the system's capabilities, we use the You Only Look Once (YOLO) for weapon detection. YOLO is a state-of-the-art object detection algorithm that operates in real-time with high accuracy. By incorporating YOLO into our system, we enhance its ability to not only detect violent activities but also identify the presence of weapons in the scene. This additional functionality adds a crucial layer of security and enables authorities to respond more effectively to potential threats. Overall, the combined approach of CNN, Bi-LSTM, and YOLO offers a comprehensive solution for real-time violence detection in video streams, with the added capability of weapon detection. This integrated system contributes to enhancing public safety and security in various settings, including public spaces, by enabling timely intervention and response to potential threats.

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Section: Resultssupporting

confidence: 91%

Real time Violence and Weapon detection and Alert System

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Patil,

Pattanshetti

2024

Preprint

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“…2 demonstrates a visual representation of our experimental results for Yolo models. Moreover, for performance evaluation, standards performance evaluation metrics, including precision, recall, and F-score, are employed inspired by [21,22]. The details of performance evaluation are discussed in the following sections.…”

Section: Resultsmentioning

confidence: 99%

An Edge Computing-based Handgun and Knife Detection Method in IoT Video Surveillance Systems

Liu,

2023

IJACSA

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Real-time handgun and knife detection on edge devices within the Internet of Things (IoT) video surveillance systems hold paramount importance in ensuring public safety and security. Numerous methods have been explored for handgun and knife detection in video-based surveillance systems, with deep learning-based approaches demonstrating superior accuracy compared to other methods. However, the current research challenge lies in achieving high accuracy rates while managing the computational demands to meet real-time requirements. This paper proposes a solution by introducing a single-stage convolutional neural network (CNN) model tailored to address this challenge. The proposed method is developed using a custom dataset, encompassing model generation, training, validation, and testing phases. Extensive experiments and performance evaluations substantiate the efficacy of the proposed approach, which achieves remarkable accuracy results, thus showcasing its potential for enhancing real-time handgun and knife and knife detection capabilities in IoT-based video surveillance systems.

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“…The reason for selecting this algorithm is its strength and ability to recognize almost any size of objects accurately in real time. 68 However, the application of YOLO is not mandatory, but it can be used for a better localization rate. As provided in Sec.…”

Section: Overview Of Anomaly Detection Methodsmentioning

confidence: 99%

“…To reduce the computational complexity for anomaly localization, only the frames containing abnormal activities are passed to the object detection algorithm YOLO v.4. The reason for selecting this algorithm is its strength and ability to recognize almost any size of objects accurately in real time 68 . However, the application of YOLO is not mandatory, but it can be used for a better localization rate.…”

Section: Overview Of Anomaly Detection Methodsmentioning

confidence: 99%

Anomaly behavior detection analysis in video surveillance: a critical review

et al. 2023

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Anomaly detection is one of the most researched topics in computer vision and machine learning. Manual detection of an oddity in a video costs significant time and money, so there is a need for an autonomous detection system that can analyze the process and detect the anomaly in the majority of captured video datasets. Through an in-depth study on the recently published works on anomaly detection, a review is prepared to highlight the various tasks performed in abnormal behavior detection. Descriptions along with the pros and cons of various machine-learning and non-machine-learning techniques are discussed in depth. Similarly, more concentration is given to the generation adversarial network (GAN), and a comprehensive description of its design for achieving a better abnormality detection rate is provided. Moreover, a comparison of various state-of-the-art approaches on the basis of their methodologies, advantages, and disadvantages is given. We further quantitatively analyze some of the recent robust approaches at the frame level on the UCSD Ped1 dataset, with the GAN-based model achieving an astonishing performance. We provide various suggestions on how to further increase the performance of GAN for abnormal behavior detection in surveillance videos.

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Intelligent Video Surveillance Based on YOLO: A Comparative Study

Cited by 10 publications

References 13 publications

Real time Violence and Weapon detection and Alert System

Real time Violence and Weapon detection and Alert System

An Edge Computing-based Handgun and Knife Detection Method in IoT Video Surveillance Systems

Anomaly behavior detection analysis in video surveillance: a critical review

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