Edge Video Analytics: A Survey on Applications, Systems and Enabling
Techniques
- URL: http://arxiv.org/abs/2211.15751v3
- Date: Wed, 11 Oct 2023 15:13:02 GMT
- Title: Edge Video Analytics: A Survey on Applications, Systems and Enabling
Techniques
- Authors: Renjie Xu, Saiedeh Razavi and Rong Zheng
- Abstract summary: Video is a key driver in the global explosion of digital information.
Governments and enterprises are deploying innumerable cameras for a variety of applications, all facilitated by video analytics (VA)
With the proliferation of Internet-connected devices, massive amounts of data are generated daily, overwhelming the cloud.
Edge computing, an emerging paradigm that moves workloads and services from the network core to the network edge, has been widely recognized as a promising solution.
- Score: 3.9134031118910264
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Video, as a key driver in the global explosion of digital information, can
create tremendous benefits for human society. Governments and enterprises are
deploying innumerable cameras for a variety of applications, e.g., law
enforcement, emergency management, traffic control, and security surveillance,
all facilitated by video analytics (VA). This trend is spurred by the rapid
advancement of deep learning (DL), which enables more precise models for object
classification, detection, and tracking. Meanwhile, with the proliferation of
Internet-connected devices, massive amounts of data are generated daily,
overwhelming the cloud. Edge computing, an emerging paradigm that moves
workloads and services from the network core to the network edge, has been
widely recognized as a promising solution. The resulting new intersection, edge
video analytics (EVA), begins to attract widespread attention. Nevertheless,
only a few loosely-related surveys exist on this topic. The basic concepts of
EVA (e.g., definition, architectures) were not fully elucidated due to the
rapid development of this domain. To fill these gaps, we provide a
comprehensive survey of the recent efforts on EVA. In this paper, we first
review the fundamentals of edge computing, followed by an overview of VA. EVA
systems and their enabling techniques are discussed next. In addition, we
introduce prevalent frameworks and datasets to aid future researchers in the
development of EVA systems. Finally, we discuss existing challenges and foresee
future research directions. We believe this survey will help readers comprehend
the relationship between VA and edge computing, and spark new ideas on EVA.
Related papers
- Networking Systems for Video Anomaly Detection: A Tutorial and Survey [56.44953602790945]
Video Anomaly Detection (VAD) is a fundamental research task within the Artificial Intelligence (AI) community.
This article offers an exhaustive tutorial for novices in NSVAD.
We showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD.
arXiv Detail & Related papers (2024-05-16T02:00:44Z) - A Survey on Occupancy Perception for Autonomous Driving: The Information Fusion Perspective [20.798308029074786]
3D occupancy perception technology aims to observe and understand dense 3D environments for autonomous vehicles.
Similar to traditional bird's-eye view (BEV) perception, 3D occupancy perception has the nature of multi-source input and the necessity for information fusion.
arXiv Detail & Related papers (2024-05-08T16:10:46Z) - Towards Generalist Robot Learning from Internet Video: A Survey [56.621902345314645]
This survey presents an overview of methods for learning from video (LfV) in the context of reinforcement learning (RL) and robotics.
We focus on methods capable of scaling to large internet video datasets.
We advocate for scalable foundation model approaches that can leverage the full range of internet video data.
arXiv Detail & Related papers (2024-04-30T15:57:41Z) - Green Edge AI: A Contemporary Survey [49.47249665895926]
We present a contemporary survey on green edge AI.
Despite its potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of deep learning (DL)
We explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference.
arXiv Detail & Related papers (2023-12-01T04:04:37Z) - Edge AI for Internet of Energy: Challenges and Perspectives [5.267662071764103]
The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI)
This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem.
arXiv Detail & Related papers (2023-11-28T15:01:56Z) - Delving into the Devils of Bird's-eye-view Perception: A Review,
Evaluation and Recipe [115.31507979199564]
Learning powerful representations in bird's-eye-view (BEV) for perception tasks is trending and drawing extensive attention both from industry and academia.
As sensor configurations get more complex, integrating multi-source information from different sensors and representing features in a unified view come of vital importance.
The core problems for BEV perception lie in (a) how to reconstruct the lost 3D information via view transformation from perspective view to BEV; (b) how to acquire ground truth annotations in BEV grid; and (d) how to adapt and generalize algorithms as sensor configurations vary across different scenarios.
arXiv Detail & Related papers (2022-09-12T15:29:13Z) - Distributed intelligence on the Edge-to-Cloud Continuum: A systematic
literature review [62.997667081978825]
This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today.
The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
arXiv Detail & Related papers (2022-04-29T08:06:05Z) - Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
We conduct a systematic review for both cloud and edge AI.
We are the first to set up the collaborative learning mechanism for cloud and edge modeling.
We discuss potentials and practical experiences of some on-going advanced edge AI topics.
arXiv Detail & Related papers (2021-11-11T05:58:23Z) - A Review on Deep Learning in UAV Remote Sensing [7.721988450630861]
We present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery.
For that, a total of 232 papers published in international scientific journal databases was examined.
We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data.
arXiv Detail & Related papers (2021-01-22T16:08:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.