mlsanityData to 7 Oct 2026

Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques

arXiv 2211.15751 ↗First posted 28 Nov 2022cs.NI cs.CV cs.LGcited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 89 of its 301 references that are in the map. First posted on arXiv 28 Nov 2022.

Builds on

89 of 301 references.

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38 more
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50 most cited of 89 All 89 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 3.

7 papers.

  1. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  2. 2026Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervisioncited by 1
  3. 2025EcoLens: Leveraging Multi-Objective Bayesian Optimization for Energy-Efficient Video Processing on Edge Devicescited by 0
  4. 2025DMTrack: Deformable State-Space Modeling for UAV Multi-Object Tracking with Kalman Fusion and Uncertainty-Aware Associationcited by 0
  5. 2025Video Object Recognition in Mobile Edge Networks: Local Tracking or Edge Detection?cited by 0
  6. 2026AVA-VLM: Adaptive Visual Attention-Vision Language Model for In-the-Wild Construction Site Monitoringcited by 0
  7. 2026Graph Neural Assisted Actor-Critic for Latency-Efficient Edge Vision Systemcited by 0