Cloud-Enabled Deep Learning Framework for Public Security Video Investigation Systems

Abstract

The incorporation of deep learning methods into public security video investigation systems is investigated in this review article with special attention to their transforming ability in improving real-time surveillance and crime prevention. With the rapid developments in machine learning and computer vision, deep learning models which includes Convolutional Neural Networks, and Recurrent Neural Networks (RNNs) have shown astonishing capacity in automating video surveillance tasks including finding objects, activity recognition, and anomaly detection. These models are highly useful for public safety operations since they enable crowd management, identification of suspicious behaviour, and even specific actions like theft or assault. Examining the technical architecture of these systems, the paper emphasises on the part edge computing and cloud computing play in allowing scalability and real-time data processing. While edge computing provides localised processing to lower latency and increase response times, cloud-based solutions guarantee perfect integration and storage of vast video information. Moreover, the study tackles the difficulties in applying deep learning in public security including privacy issues, data security, ethical questions, and the necessity of laws. Notwithstanding these difficulties, the research underlines how these technologies might help to enhance security operations, lower human error, and raise operational efficiency. Future research directions—such as improving model robustness, combining multimodal data sources, and creating more ethical and transparent artificial intelligence systems—also come out of the review. In the end, this paper offers a thorough summary of the present situation and future possibilities of deep learning in public security video investigation systems, so illuminating their ability to change the scene of public safety.

Introduction

Video Investigation Systems are advanced technologies used in law enforcement, forensics, and security to analyze video data using AI and machine learning. These systems offer real-time and archival analysis through capabilities like facial recognition, object detection, motion tracking, and behavior analysis. They support event correlation across multiple video sources, enhance low-quality footage, and ensure data integrity with cloud-based platforms.

Key Features & Technologies:

• AI/ML Integration: Improves detection accuracy, anomaly identification, and pattern recognition using deep learning models such as CNNs (for object/face/license plate recognition) and RNNs (for activity prediction).

• Cloud Computing: Provides scalable storage and high-speed processing of video feeds from CCTV, drones, and bodycams. Enables real-time data streaming and multi-agency collaboration with secure access controls.

• Edge Computing: Complements cloud resources by processing data locally at the source, especially useful in low-connectivity areas to reduce bandwidth and latency.

• Video Enhancement Tools: Includes noise reduction, resolution improvement, and video summarization to support efficient video review and deep learning analysis.

• IoT & NLP Integration: Smart devices and sensors contribute to situational awareness, while NLP helps auto-generate metadata and streamline video data management.

Ethical & Legal Compliance:

Systems incorporate privacy-preserving measures (e.g., anonymization, differential privacy, encrypted access), automated auditing, and public education initiatives to build trust and ensure ethical surveillance.

Applications:

• Crime scene analysis

• Traffic and crowd monitoring

• Incident response

• Criminal identification and evidence sharing

Benefits:

• Faster response times

• More accurate investigations

• Resource efficiency via automation

• Predictive analytics for proactive threat prevention

• Future scalability with support for emerging technologies like quantum computing

Related Work Highlights:

• Ibrahim et al. (2022): Used CNNs to classify encrypted images with high accuracy without decryption.

• Attaallah (2022): Studied data security in healthcare using Fuzzy AHP for evaluating big data protection strategies.

• Minaee et al. (2022): Reviewed deep learning methods for image segmentation relevant to surveillance.

• Chen et al. (2022): Addressed privacy/security in the Metaverse.

• Reed et al. (2022): Highlighted the role of cloud computing and AI in next-gen high-performance computing.

Conclusion

Last but not least, this review piece highlights how automated video investigation systems powered by deep learning could change public safety. When coupled with other deep learning methodologies, advanced models such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) have significantly improved the accuracy and efficiency of security surveillance systems. By automating tasks like object detection, activity recognition, and anomaly detection, deep learning systems enable real-time, proactive responses to security threats. This reduces reliance on human operators and minimises response times. These systems are scalable, thanks to cloud and edge computing, so they can handle huge datasets from a variety of video sources without any hitches, ensuring complete coverage of public spaces. The ability of deep learning models to ingest massive amounts of data ensures that their accuracy and performance are always improving, making them ideal for environments that are unpredictable. Concerns around data security, privacy, and ethics must be thoroughly investigated in order to ensure the ethical use of new technologies. Open regulations and transparent regulatory frameworks are crucial for balancing security needs with personal privacy rights. Public safety video investigation systems powered by deep learning represent a huge leap forward in security technology since they hold the promise of smarter, more efficient, and safer public environments. Crime prevention, real-time surveillance, and public safety as a whole stand to benefit greatly from their incorporation into many public and private sectors, especially as these systems evolve. In order to enhance their impact on global security initiatives, future research and development should focus on enhancing these systems, resolving ethical concerns, and expanding their applications.

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Copyright

Copyright © 2025 Varunendra Sharma, Unmukh Datta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.