Abnormal activity detection in surveillance video scenes

Ali, Jwan Jamal; Shati, Narjis Mezaal; Gaata, Methaq Talib

doi:10.12928/telkomnika.v18i5.16634

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…In addition, compared with manual detection, the advantage of using artificial intelligence (AI) to identify microexpressions is that as long as the camera objectively captures the identified object, the computer will have obtained the relevant data for processing [16]. In other words, by installing cameras, a certain level of recognition can be achieved [16], [17]. In this study, we used FaceReader, professional facial expression analysis software, for microexpression recognition.…”

Section: Methodsmentioning

confidence: 99%

Microexpression recognition robot

Wu¹,

Liu²,

Tsao³

et al. 2023

IJRA

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Following the development of big data, the use of microexpression technology has become increasingly popular. The application of microexpressions has expanded beyond medical treatment to include scientific case investigations. Because microexpressions are characterized by short duration and low intensity, training humans to recognize their yields poor performance results. Automatically recognizing microexpressions by using machine learning techniques can provide more effective results and save time and resources. In the real world, to avoid judicial punishment, people lie and conceal the truth for a variety of reasons. In this study, our primary objective was to develop a system for real-time microexpression recognition. We used FaceReader as the retrieval system and integrated the data with an application programming interface to provide recognition results as objective references in real-time. Using an experimental analysis, we also attempted to determine the optimal system configuration conditions. In conclusion, the use of artificial intelligence is expected to enhance the efficiency of investigations.

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Section: Methodsmentioning

confidence: 99%

Microexpression recognition robot

Wu¹,

Liu²,

Tsao³

et al. 2023

IJRA

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“…Wassim et al [11] used a feature approach to detect abnormal activities in crowded scenes on the UCSD anomaly detection dataset. The first category is motion features calculated using optical flow; the second is the size of moving individuals within frames; and the third is motion magnitude.…”

Section: Visual Analytics and Surveillance Systemsmentioning

confidence: 99%

Edge-Computing-Enabled Abnormal Activity Recognition for Visual Surveillance

Ali,

Goyal,

Sharma

et al. 2024

Electronics

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Due to the ever increasing number of closed circuit television (CCTV) cameras worldwide, it is the need of the hour to automate the screening of video content. Still, the majority of video content is manually screened to detect some anomalous incidence or activity. Automatic abnormal event detection such as theft, burglary, or accidents may be helpful in many situations. However, there are significant difficulties in processing video data acquired by several cameras at a central location, such as bandwidth, latency, large computing resource needs, and so on. To address this issue, an edge-based visual surveillance technique has been implemented, in which video analytics are performed on the edge nodes to detect aberrant incidents in the video stream. Various deep learning models were trained to distinguish 13 different categories of aberrant incidences in video. A customized Bi-LSTM model outperforms existing cutting-edge approaches. This approach is used on edge nodes to process video locally. The user can receive analytics reports and notifications. The experimental findings suggest that the proposed system is appropriate for visual surveillance with increased accuracy and lower cost and processing resources.

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“…Comparing Open-Source Federated Learning Frameworks for IoT: A Review and Analysis The scientific study compares open-source Federated Learning Frameworks and their usefulness in IoT systems. (Ali et al, 2020; TensorFlow Federated, Paddle Federated Learning Framework, and PySyft are open-source federated learning frameworks.…”

Section: Federated Learningmentioning

confidence: 99%

Secure Federated Learning with a Homomorphic Encryption Model

Hussien,

Salman

et al. 2023

IJPASR

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Federated learning (FL) offers collaborative machine learning across decentralized devices while safeguarding data privacy. However, data security and privacy remain key concerns. This paper introduces "Secure Federated Learning with a Homomorphic Encryption Model," addressing these challenges by integrating homomorphic encryption into FL. The model starts by initializing a global machine learning model and generating a homomorphic encryption key pair, with the public key shared among FL participants. Using this public key, participants then collect, preprocess, and encrypt their local data. During FL Training Rounds, participants decrypt the global model, compute local updates on encrypted data, encrypt these updates, and securely send them to the aggregator. The aggregator homomorphic ally combines updates without revealing participant data, forwarding the encrypted aggregated update to the global model owner. The Global Model Update ensures the owner decrypts the aggregated update using the private key, updates the global model, encrypts it with the public key, and shares the encrypted global model with FL participants. With optional model evaluation, training can iterate for several rounds or until convergence. This model offers a robust solution to Florida data privacy and security issues, with versatile applications across domains. This paper presents core model components, advantages, and potential domain-specific implementations while making significant strides in addressing FL's data privacy concerns.

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Abnormal activity detection in surveillance video scenes

Cited by 8 publications

References 14 publications

Microexpression recognition robot

Microexpression recognition robot

Edge-Computing-Enabled Abnormal Activity Recognition for Visual Surveillance

Secure Federated Learning with a Homomorphic Encryption Model

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