HYPOTHETICAL APPROACH FOR ADVANCING AI-ENABLED VIDEO PROCESSING IN CLOUD-BASED SURVEILLANCE SYSTEMS - Nadaf, Jubber; Kadam, Amol K.; Wakurdekar, Sachin; Patil, T. B. (ShodhKosh: Journal of Visual and Performing Arts, 2023)
This abstract presents an overview of the transformative potential of integrating IoT, AI, and ML technologies in cloud-based surveillance systems for audio/video processing. The convergence of these technologies has led to the development of intelligent surveillance solutions capable of enhancing security measures through real-time detection and response to threats. By employing advanced algorithms such as object detection, facial recognition, and anomaly detection, businesses can significantly improve the accuracy and efficiency of surveillance operations. Platforms like Amazon SageMaker and Google Cloud AI Platform offer the infrastructure and tools necessary for training and deploying these algorithms, enabling organizations to tailor surveillance systems to their specific requirements. Looking towards the future, the integration of edge computing technology with cloud-based surveillance systems holds promise for further innovation and efficiency gains. The research carried out explores automation in self-regulating video surveillance using cloud computing, microservices, and advanced data processing techniques. The primary focus is on optimizing traffic management and incident detection through AI and IoT integration. This paper introduces an efficient architectural framework and mathematical formulations for real-time data analysis, storage, and retrieval, ensuring improved performance and automation. The ultimate goal is to create a safer and more secure environment by leveraging AI-enabled video processing to mitigate security threats effectively. This abstract underscores the importance of ongoing research and collaboration in harnessing the full potential of AI-enabled video processing for audio/video surveillance in cloud-based systems.