Robust learning for real-world anomalies in surveillance videos

Mumtaz, Aqib; Sargano, Allah Bux; Habib, Zulfiqar

doi:10.1007/s11042-023-14425-x

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…• Smart City security: Surveillance and CCTV systems equipped with AI have significantly improved security in smart cities. Modern video analysis tools use sophisticated machine learning algorithms (e.g., Convolutional Neural Networks (CNNs) [104], [105], Recurrent Neural Networks (RNNs) [106], [107], Support Vector Ma- Ref.…”

Section: A Industry-specific Use Casesmentioning

confidence: 99%

Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

Humayun,

Tariq,

Alfayad

et al. 2024

IEEE Access

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The Internet of Things (IoT) has revolutionized various domains, enabling interconnected devices to communicate and exchange data. The integration of Artificial Intelligence (AI) in IoT systems further enhances their capabilities and potential benefits. Unfortunately, in the era of AI, ensuring the privacy and security of the IoT faces novel and specific challenges. IoT security is imperative, necessitating comprehensive strategies, including comprehension of IoT security challenges, implementation of AI methodologies, adoption of resilient security frameworks, and handling of privacy and ethical concerns to construct dependable and secure IoT systems. It is vital to note that the term 'security' encompasses a more comprehensive view than cyberattacks alone. Therefore, with an emphasis on securing against cyberattacks, this comprehensive survey also includes physical security threats on the IoT. It investigates the complexities and solutions for IoT systems, placing particular emphasis on AI-based security techniques. The paper undertakes a categorization of the challenges associated with ensuring IoT security, investigates the utilization of AI in IoT security, presents security frameworks and strategies, underscores privacy and ethical considerations, and provides insights derived from practical case studies. Furthermore, the survey sheds light on emerging trends concerning IoT security in the AI era. This survey provides significant contributions to the understanding of establishing dependable and secure IoT systems through an exhaustive examination of the present condition of IoT security and the ramifications of AI on it.

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Section: A Industry-specific Use Casesmentioning

confidence: 99%

Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

Humayun,

Tariq,

Alfayad

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Approach Techniques Dataset [27] Deep learning DCNN UCSD, CUHK, ShanghaiTech [28] Deep Learning DTM technique UCSD, Mall, UMN and MED [29] Deep Learning Neural Network models Custom dataset [30] Deep Learning SFE technique TUT 2016 [31] Deep Learning and Bio-Inspired CRN along with AntHocNet Custom dataset [32] Deep Learning IIN UCF-Crime, UCSD [33] AI DCNN Custom dataset [34] Deep Learning DCNN methods Seven benchmark datasets [35] Deep Learning DNN UCF-Crime [36] Deep Learning LMNN Custom dataset…”

Section: Referencesmentioning

confidence: 99%

VidAnomalyNet: An Efficient Anomaly Detection in Public Surveillance Videos Through Deep Learning Architectures

Chidananda,

Kumar

2024

IJSSE

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“…The advent of depth-sensing camera technologies has played a transformative role by enabling the acquisition of comprehensive 3D human body structure and posture data, providing a more holistic representation of human actions. As artificial intelligence capabilities advanced, DL algorithms, notably Convolutional Neural Networks (CNNs), have arisen as potent tools for both feature extraction and classification tasks [5]. CNNs have a notable advantage in autonomously learning high-level features from raw data, eliminating the need for complex manual feature engineering.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Surveillance Framework for Physical Abuse Detection

2023

Preprint

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Physical abuse is a societal problem irrespective of whether the society is rich, educated or so. Mostly physically weaker section like children, women and old age people are most vulnerable to it especially in cases of domestic violence or workplace aggression. If the abuser and victim share a relationship then reporting the abuse becomes all the more challenging. Hence an intelligent surveillance system is the need of the hour. In this paper we propose a hybrid deep learning framework for physical abuse detection and prevention through human action recognition. 3D convolution neural network (CNN) based deep learning model is built to analyze this complex human action. Using transfer learning ResNet-18 and GoogleNet model are trained for publicly available UBI fight and UCF crime dataset for detecting physical abuse in the video. The 2D kernels are converted to 3D kernels for both temporal and spatial feature extraction in the video. A layer of bilinear LSTM is added to gather long term temporal information, so that there is improvement in capturing the human action information. This hybrid model used for physical abuse detection is the novel approach and the results show that the performance parameters such as precision, accuracy, specificity and sensitivity score improved as we moved from 2D kernels to 3D, and there is remarkable improvement when bilinear LSTM was added to the deep learning model.

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Robust learning for real-world anomalies in surveillance videos

Cited by 4 publications

References 30 publications

Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

Securing the Internet of Things in Artificial Intelligence Era: A Comprehensive Survey

VidAnomalyNet: An Efficient Anomaly Detection in Public Surveillance Videos Through Deep Learning Architectures

Intelligent Surveillance Framework for Physical Abuse Detection

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