Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead
The falling cost of IoT cameras, the advancement of AI-based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled the widespread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. W...
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
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| Series: | IoT |
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| Online Access: | https://www.mdpi.com/2624-831X/4/4/21 |
| _version_ | 1797380630860791808 |
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| author | Arun A. Ravindran |
| author_facet | Arun A. Ravindran |
| author_sort | Arun A. Ravindran |
| collection | DOAJ |
| description | The falling cost of IoT cameras, the advancement of AI-based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled the widespread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely performed in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between state-of-the-art computer vision algorithms and the successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on IoT edge streaming video analytics (IE-SVA) systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of IE-SVA systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short-, medium-, and long-term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the evolution of IE-SVA systems. |
| first_indexed | 2024-03-08T20:40:01Z |
| format | Article |
| id | doaj.art-9ebd390eda36426cbfe1124f5bc8b124 |
| institution | Directory Open Access Journal |
| issn | 2624-831X |
| language | English |
| last_indexed | 2024-03-08T20:40:01Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | IoT |
| spelling | doaj.art-9ebd390eda36426cbfe1124f5bc8b1242023-12-22T14:16:35ZengMDPI AGIoT2624-831X2023-10-014448651310.3390/iot4040021Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths AheadArun A. Ravindran0Department of Electrical and Computer Engineering, University of North Carolina at Charlotte, Charlotte, NC 28223, USAThe falling cost of IoT cameras, the advancement of AI-based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled the widespread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely performed in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between state-of-the-art computer vision algorithms and the successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on IoT edge streaming video analytics (IE-SVA) systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of IE-SVA systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short-, medium-, and long-term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the evolution of IE-SVA systems.https://www.mdpi.com/2624-831X/4/4/21video analyticsedge computingstreaming videosystemsdeep learningAI |
| spellingShingle | Arun A. Ravindran Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead IoT video analytics edge computing streaming video systems deep learning AI |
| title | Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead |
| title_full | Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead |
| title_fullStr | Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead |
| title_full_unstemmed | Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead |
| title_short | Internet-of-Things Edge Computing Systems for Streaming Video Analytics: Trails Behind and the Paths Ahead |
| title_sort | internet of things edge computing systems for streaming video analytics trails behind and the paths ahead |
| topic | video analytics edge computing streaming video systems deep learning AI |
| url | https://www.mdpi.com/2624-831X/4/4/21 |
| work_keys_str_mv | AT arunaravindran internetofthingsedgecomputingsystemsforstreamingvideoanalyticstrailsbehindandthepathsahead |