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Virtual Surveillance Assistant: Converging Computer Vision, NLP, and Chatbot Technologies via Azure Cognitive Services

Published: 13 May 2024 Publication History

Abstract

In the ever-evolving landscape of heightened security concerns, traditional surveillance systems are revealing their limitations in effectively addressing the complexities of modern security challenges. This paper presents a pioneering solution in the form of a sophisticated Virtual Surveillance Assistant, a revolutionary system that seamlessly integrates cutting-edge technologies. By harnessing the power of computer vision, Natural Language Processing (NLP), and chatbot technologies, this project stands as a testament to innovation in security enhancement. Bolstered by the formidable capabilities of Azure Cognitive Services, the Virtual Surveillance Assistant endeavors to combat security vulnerabilities through a harmonious fusion of automated processes, real-time monitoring, multilingual communication, and intelligent threat detection. The work proposed offers a unique and effective combination of chatbot integrated with the computer vision, NLP, and speech/text processing components to leverage their functionalities. Integration points are established to enable seamless communication between the chatbot and other system components. Computer vision component achieves an accuracy of 99.7 % and precision of 94.4%. The results obtained were analyzed to identify any potential limitations or areas for improvement, such as handling occlusions, variations in lighting conditions, and complex backgrounds. Virtual Surveillance Assistant charts a course towards an intelligent, proactive, and vigilant security ecosystem.

References

[1]
Microsoft Azure Cognitive Services, "Computer Vision - Extract rich information from images and videos," Microsoft Corporation, 2021. [Online]. Available: https://azure.microsoft.com/en-us/services/cognitive-services/computer-vision/
[2]
Microsoft Azure Cognitive Services, "Face API - Detect, identify, analyze, organize, and tag faces in photos," Microsoft Corporation, 2021. [Online]. Available: https://azure.microsoft.com/en-us/services/cognitive-services/face/
[3]
Microsoft Azure Cognitive Services, "Custom Vision - Build and deploy custom machine learning models," Microsoft Corporation, 2021. [Online]. Available: https://azure.microsoft.com/en-us/services/cognitive-services/custom-vision-service/
[4]
Microsoft Azure Cognitive Services, "Text Analytics - Microsoft Azure," Microsoft Corporation, 2021. [Online]. Available: https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/
[5]
Microsoft Azure Cognitive Services, "Speech Services - Convert spoken language into written text and back again," Microsoft Corporation, 2021. [Online]. Available: https://azure.microsoft.com/en-us/services/cognitive-services/speech-services/
[6]
N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
[7]
A. M. López, "OpenCV: Computer Vision Projects with Python," Packt Publishing, 2016.
[8]
Olivier, B., Droogenbroeck, M.V.: Vibe: a universal background subtraction algorithm for video sequences. In: IEEE Transactions on Image processing, vol. 20, no. 6, pp. 1709–1724, 2011.
[9]
Sieradzki, R., Grega, M., Lach, S.: Automated recognition of firearms in surveillance video. In: IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), San Diego, CA, USA, pp. 45–50, 2013.
[10]
Megherbi, N., Flitton, G.T., Breckon, T.P.: A classifier based approach for the detection of potential threats in ct based baggage screening. In: 17th IEEE International Conference on Image Processing (ICIP), Hong Kong, China, pp. 1833–1836, 2010.
[11]
Akcay, C., Kundergorski, M.E., Devereux, M., Breckon, T.P.: Transfer learning using convolutional neural networks for object classification within X-ray baggage security imagery. In: IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, pp. 1057–1061, 2016.
[12]
Merry, D., Mondragon, G., Riffo, V., Zuccar, I.: Detection of regular objects in baggage using multiple X-ray views. Insight-Non-Destr. Test. Cond. Monit. 55(1), 16–20, 2013.
[13]
Roomi, M., Rajashankarii, R.: Detection of concealed weapons in X-ray images using fuzzy k-nn. Int. J. Comput. Sci. Eng. Inf. Technol. 2(2), 65–70, 2012.
[14]
Lai, J., Maples, S.: Developing a real-time gun detection classifier. Accessed on August 2017). http://cs231n.stanford.edu/reports/2017/pdfs/716.pdf
[15]
O'Reilly, D., Bowring, N., Harmer, S.: Signal processing techniques for concealed weapon detection by use of neural networks. In: IEEE 27th Convention of Electrical & Electronics Engineers in Israel (IEEEI), Eilat, Israel, pp. 1–4, 2012.
[16]
Olsmos, R., Tabik, S., Herrera, F.: Automatic handgun detection alarm in videos using deep learning. Neurocomputing 275, 66–72, 2018.
[17]
A. Graves, A.-r. Mohamed, and G. Hinton, "Speech Recognition with Deep Recurrent Neural Networks," in IEEE International Conference on Acoustics, Speech and Signal Processing, 2013.
[18]
A. Papangelis and F. Schaub, "A Review of Challenges and Opportunities in Video Analytics," in ACM Transactions on Multimedia Computing, Communications, and Applications, 2020.
[19]
Gupta, S., "NLP Techniques for Language Detection and Translation in Surveillance Systems." Journal of Security and Communication Systems, 8(3), 145-157, 2020.
[20]
Liu, M., "Chatbot Integration in Surveillance Systems for Real-time Information Retrieval." International Conference on Intelligent Surveillance Systems, Proceedings, 78-89, 2018.
[21]
Kim, S., "Enhancing Security Systems with Interactive Chatbots for Incident Reporting and Emergency Response." Journal of Security Technology, 16(4), 213-226, 2020.
[22]
Jha, Mahesh Kumar, "Converge of IoT and AI in Metaverse: Challenges and Opportunities." 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2023.
[23]
Chandwani, Akshaj, "Virtual Assistant for Appointment Booking." 2023 IEEE 8th International Conference for Convergence in Technology (I2CT). IEEE, 2023.
[24]
Cui, Xiaoliang, "Fusing surveillance videos and three‐dimensional scene: A mixed reality system." Computer Animation and Virtual Worlds 34.1 (2023): e2129.
[25]
Roy, Subhrajit, and Binoy Das. "Understanding the Motion Adaption of Machine Using Long Short–Term Memory Networks for voiceless Virtual Assistant." International Journal of Digital Technologies 2.1 (2023).
[26]
Sriram, V. P., "Design of voice based virtual assistant using internet of things." 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2022.

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    ICIMMI '23: Proceedings of the 5th International Conference on Information Management & Machine Intelligence
    November 2023
    1215 pages
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    Published: 13 May 2024

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