# Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniques arXiv: https://arxiv.org/abs/2211.15751 ยท HTML: https://mlsanity.com/paper/2211.15751/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 89 of its 301 references that are in the map. - First posted on arXiv 28 Nov 2022. ## Builds on (most cited first, 30 of 89) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014) - [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015) - [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015) - [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017) - [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015) - [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018) - [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016) - [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018) - [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017) - [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016) - [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021) - [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020) - [In-Datacenter Performance Analysis of a Tensor Processing Unit](https://mlsanity.com/paper/1704.04760/) (2017) - [BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks](https://mlsanity.com/paper/1709.01686/) (2017) - [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016) - [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018) - [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020) - [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017) - [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020) - [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019) - [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018) - [Recent Advances in Convolutional Neural Networks](https://mlsanity.com/paper/1512.07108/) (2015) - [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020) - [Towards Real-Time Multi-Object Tracking](https://mlsanity.com/paper/1909.12605/) (2019) - [FFA-Net: Feature Fusion Attention Network for Single Image Dehazing](https://mlsanity.com/paper/1911.07559/) (2019) - [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019) - [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019) - [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018) - [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014) ## Built on by (most cited first, 7 of 7) - [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024) - [Benchmarking Compact VLMs for Clip-Level Surveillance Anomaly Detection Under Weak Supervision](https://mlsanity.com/paper/2603.13306/) (2026) - [EcoLens: Leveraging Multi-Objective Bayesian Optimization for Energy-Efficient Video Processing on Edge Devices](https://mlsanity.com/paper/2506.00754/) (2025) - [DMTrack: Deformable State-Space Modeling for UAV Multi-Object Tracking with Kalman Fusion and Uncertainty-Aware Association](https://mlsanity.com/paper/2510.17860/) (2025) - [Video Object Recognition in Mobile Edge Networks: Local Tracking or Edge Detection?](https://mlsanity.com/paper/2511.20716/) (2025) - [AVA-VLM: Adaptive Visual Attention-Vision Language Model for In-the-Wild Construction Site Monitoring](https://mlsanity.com/paper/2607.05859/) (2026) - [Graph Neural Assisted Actor-Critic for Latency-Efficient Edge Vision System](https://mlsanity.com/paper/2608.16142/) (2026) ## 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.