Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things

Pazho, Armin Danesh; Neff, Christopher; Noghre, Ghazal Alinezhad; Ardabili, Babak Rahimi; Yao, Shanle; Baharani, Mohammadreza; Tabkhi, Hamed

doi:10.1109/jiot.2023.3263725

Cited by 19 publications

(1 citation statement)

References 53 publications

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“…Several studies [25][26][27][28] on anomaly behavior detection have focused mostly on improving the accuracy of DL models, making them tiny on edge devices. In [29][30][31], the authors analyzed the challenges and potential of DL-based pose anomaly detection in video analysis, emphasizing the advantages in terms of privacy and computational efficiency. Nevertheless, these studies did not consider the hierarchical structure of edge computing systems or the practical considerations related to delivering real-time video surveillance services.…”

Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework

Bazarov Ravshan Ugli,

Mohammed,

et al. 2024

Sensors

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Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains.

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Section: Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework

Bazarov Ravshan Ugli,

Mohammed,

et al. 2024

Sensors

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Understanding Policy and Technical Aspects of AI-enabled Smart Video Surveillance to Address Public Safety

et al. 2023

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Recent advancements in artificial intelligence (AI) have seen the emergence of smart video surveillance (SVS) in many practical applications, particularly for building safer and more secure communities in our urban environments. Cognitive tasks, such as identifying objects, recognizing actions, and detecting anomalous behaviors, can produce data capable of providing valuable insights to the community through statistical and analytical tools. However, artificially intelligent surveillance systems design requires special considerations for ethical challenges and concerns. The use and storage of personally identifiable information (PII) commonly pose an increased risk to personal privacy. To address these issues, this paper identifies the privacy concerns and requirements needed to address when designing AI-enabled smart video surveillance. Further, we propose the first end-to-end AI-enabled privacy-preserving smart video surveillance system that holistically combines computer vision analytics, statistical data analytics, cloud-native services, and end-user applications. Finally, we propose quantitative and qualitative metrics to evaluate intelligent video surveillance systems. The system shows the 17.8 frame-per-second (FPS) processing in extreme video scenes. However, considering privacy in designing such a system results in preferring the pose-based algorithm to the pixel-based one. This choice resulted in dropping accuracy in both action and anomaly detection tasks. The results drop from 97.48% to 73.72% in anomaly detection and 96% to 83.07% in the action detection task. On average, the latency of the end-to-end system is 36.1 seconds.

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IoT video analytics for surveillance-based systems in smart cities

Aminiyeganeh,

Coutinho,

Boukerche

2024

Computer Communications

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Ancilia: Scalable Intelligent Video Surveillance for the Artificial Intelligence of Things

Cited by 19 publications

References 53 publications

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework

Understanding Policy and Technical Aspects of AI-enabled Smart Video Surveillance to Address Public Safety

IoT video analytics for surveillance-based systems in smart cities

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