Enabling Adaptive Deep Neural Networks for Video Surveillance in Distributed Edge Clouds

Wu, Qiong; Zhang, Haitao; Du, Peilun; Li, Ye; Guo, J.; He, Chenze

doi:10.1109/icpads47876.2019.00080

Cited by 2 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With a pre-trained and trained model, the technique can achieve a recognition precision of 89% and 94%, respectively. The authors in [95] proposed a smart video monitoring task allocation challenge that reduced the overall reaction time while achieving job performance expectations due to the inadequate computational power of the shared edge clouds with video surveillance. In addition, an adaptive deep neural networks (DNNs) model selection method was proposed, which compared the similarity between the feature and part of the included video and default training films to select each of the most effective DNN schemes.…”

Section: Use Case 1: Intelligent Multimedia Processing On Edge For Su...mentioning

confidence: 99%

Artificial Intelligence (AI) and Machine Learning for Multimedia and Edge Information Processing

et al. 2022

View full text Add to dashboard Cite

The advancements and progress in artificial intelligence (AI) and machine learning, and the numerous availabilities of mobile devices and Internet technologies together with the growing focus on multimedia data sources and information processing have led to the emergence of new paradigms for multimedia and edge AI information processing, particularly for urban and smart city environments. Compared to cloud information processing approaches where the data are collected and sent to a centralized server for information processing, the edge information processing paradigm distributes the tasks to multiple devices which are close to the data source. Edge information processing techniques and approaches are well suited to match current technologies for Internet of Things (IoT) and autonomous systems, although there are many challenges which remain to be addressed. The motivation of this paper was to survey these new paradigms for multimedia and edge information processing from several technological perspectives including: (1) multimedia analytics on the edge empowered by AI; (2) multimedia streaming on the intelligent edge; (3) multimedia edge caching and AI; (4) multimedia services for edge AI; and (5) hardware and devices for multimedia on edge intelligence. The review covers a wide spectrum of enabling technologies for AI and machine learning for multimedia and edge information processing.

show abstract

Section: Use Case 1: Intelligent Multimedia Processing On Edge For Su...mentioning

confidence: 99%