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A smart vista-lite system for anomaly detection and motion prediction for video surveillance in vibrant urban settings

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A Correction to this article was published on 20 January 2025

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Abstract

The Vista-Lite system labels major challenges in video surveillance involving computational complexity, restricted transferability over datasets, and the absence of an impactful approach to examine data from various cameras. This system emphasis three methodologies to solve these issues UOAL, TempoNet and BDSO. UOAL identifies abnormalities in video content via a segmentation approach improving accuracy in complex environments. TempoNet concentrates on forecasting motions and behaviors utilizing modern neural network frameworks, enhancing response times in identifying possibly malicious situations. BDSO enhances the computational resources by tuning system parameters thus assuring flexibility and decreasing false alarms. This fusion improves system persistence, sensibility and functional cost-efficiency making the solution versatile to vast surveillance scenarios. Comprehensive experiments using pedestrian, UCSD, and mall datasets established increased performance with 99% accuracy indicating the system’s capacity to maintain real-time, multi-camera data. Vista-Lite provides a novel, innovative, flexible approach to video surveillance combining anomaly detection, motion prediction, and resource optimization for improving and enhancing the domain.

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Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. All data generated or analyzed during this study are included in this published article.

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Acknowledgments

The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Research Project under grant number RGP.1/321/45.

Funding

This work is funded by the Deanship of Research and Graduate Studies at King Khalid University under grant number RGP.1/321/45.

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Authors and Affiliations

  1. Department of Informatics and Computer Systems, College of Computer Science, King Khalid University, Abha, Saudi Arabia

    Areej Alasiry

  2. Department of Computer Engineering, College of Computer Science, King Khalid University, Abha, Saudi Arabia

    Mohammed Qayyum

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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Dr. Areej Alasiry. The first draft of the manuscript was written by Mr. Mohammed Qayyum and Dr. Areej commented on previous versions of the manuscript. The author Dr. Areej Alasiry involved in the background study of the paper and helped the mathematical derivations. Also technically involved and provided a factual review and helped edit the manuscript. All authors read and approved the final manuscript.

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Correspondence to Mohammed Qayyum.

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The authors declare no competing interests.

Ethical Statement

I will conduct myself with integrity, fidelity, and honesty. I will openly take responsibility for my actions, and only make agreements, which I intend to keep. I will not intentionally engage in or participate in any form of malicious harm to another person or animal.

Informed Consent for data Used

All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki. I consent to participate in the research project, and the following has been explained to me: the research may not be of direct benefit to me. My participation is completely voluntary. My right to withdraw from the study at any time without any implications to me.

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Alasiry, A., Qayyum, M. A smart vista-lite system for anomaly detection and motion prediction for video surveillance in vibrant urban settings. J Supercomput 81, 297 (2025). https://doi.org/10.1007/s11227-024-06753-y

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  • DOI: https://doi.org/10.1007/s11227-024-06753-y

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