# Distributed Deep Learning Model for Intelligent Video Surveillance Systems with Edge Computing

arXiv: https://arxiv.org/abs/1904.06400 · HTML: https://mlsanity.com/paper/1904.06400/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2019.

## Built on by (most cited first, 28 of 28)

- [Flash-VStream: Memory-Based Real-Time Understanding for Long Video Streams](https://mlsanity.com/paper/2406.08085/) (2024)
- [Thinking With Videos: Multimodal Tool-Augmented Reinforcement Learning for Long Video Reasoning](https://mlsanity.com/paper/2508.04416/) (2025)
- [Flash-VStream: Efficient Real-Time Understanding for Long Video Streams](https://mlsanity.com/paper/2506.23825/) (2025)
- [Adaptive Focus for Efficient Video Recognition](https://mlsanity.com/paper/2105.03245/) (2021)
- [AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition](https://mlsanity.com/paper/2112.14238/) (2021)
- [AdaFocusV3: On Unified Spatial-temporal Dynamic Video Recognition](https://mlsanity.com/paper/2209.13465/) (2022)
- [Towards Efficient and Stable K-Asynchronous Federated Learning with Unbounded Stale Gradients on Non-IID Data](https://mlsanity.com/paper/2203.01214/) (2022)
- [Efficient Asynchronous Federated Learning with Sparsification and Quantization](https://mlsanity.com/paper/2312.15186/) (2023)
- [Uni-AdaFocus: Spatial-temporal Dynamic Computation for Video Recognition](https://mlsanity.com/paper/2412.11228/) (2024)
- [HalluciDet: Hallucinating RGB Modality for Person Detection Through Privileged Information](https://mlsanity.com/paper/2310.04662/) (2023)
- [Anomaly Detection using Edge Computing in Video Surveillance System: Review](https://mlsanity.com/paper/2107.02778/) (2021)
- [Look More but Care Less in Video Recognition](https://mlsanity.com/paper/2211.09992/) (2022)
- [Spatio-temporal Prompting Network for Robust Video Feature Extraction](https://mlsanity.com/paper/2402.02574/) (2024)
- [MiPa: Mixed Patch Infrared-Visible Modality Agnostic Object Detection](https://mlsanity.com/paper/2404.18849/) (2024)
- [Modality Translation for Object Detection Adaptation Without Forgetting Prior Knowledge](https://mlsanity.com/paper/2404.01492/) (2024)
- [Median Pixel Difference Convolutional Network for Robust Face Recognition](https://mlsanity.com/paper/2205.15867/) (2022)
- [Resolution based Feature Distillation for Cross Resolution Person Re-Identification](https://mlsanity.com/paper/2109.07871/) (2021)
- [L3-Net Deep Audio Embeddings to Improve COVID-19 Detection from Smartphone Data](https://mlsanity.com/paper/2205.07682/) (2022)
- [Motivating Learners in Multi-Orchestrator Mobile Edge Learning: A Stackelberg Game Approach](https://mlsanity.com/paper/2109.12409/) (2021)
- [A Fast Horizon Detector and a New Annotated Dataset for Maritime Video Processing](https://mlsanity.com/paper/2110.13694/) (2021)
- [Real-World Community-in-the-Loop Smart Video Surveillance -- A Case Study at a Community College](https://mlsanity.com/paper/2303.12934/) (2023)
- [Advancements In Crowd-Monitoring System: A Comprehensive Analysis of Systematic Approaches and Automation Algorithms: State-of-The-Art](https://mlsanity.com/paper/2308.03907/) (2023)
- [Hybrid Transformer for Early Alzheimer's Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features](https://mlsanity.com/paper/2410.10547/) (2024)
- [Deep Learning Model with GA based Feature Selection and Context Integration](https://mlsanity.com/paper/2204.06189/) (2022)
- [ADDAI: Anomaly Detection using Distributed AI](https://mlsanity.com/paper/2205.01231/) (2022)
- [Real-time fall detection based on vision for low-power edge platforms](https://mlsanity.com/paper/2607.12909/) (2026)
- [Para-Pipe: Exploiting Hierarchical Operator Parallelism of ML Computational Graphs on SoCs](https://mlsanity.com/paper/2609.04168/) (2026)
- [VideoScout: Learning Agentic Active Exploration with Adaptive Reasoning Pacing for Long Video Understanding](https://mlsanity.com/paper/2609.15606/) (2026)

## Abstract (arXiv)

In this paper, we propose a Distributed Intelligent Video Surveillance (DIVS) system using Deep Learning (DL) algorithms and deploy it in an edge computing environment. We establish a multi-layer edge computing architecture and a distributed DL training model for the DIVS system. The DIVS system can migrate computing workloads from the network center to network edges to reduce huge network communication overhead and provide low-latency and accurate video analysis solutions. We implement the proposed DIVS system and address the problems of parallel training, model synchronization, and workload balancing. Task-level parallel and model-level parallel training methods are proposed to further accelerate the video analysis process. In addition, we propose a model parameter updating method to achieve model synchronization of the global DL model in a distributed EC environment. Moreover, a dynamic data migration approach is proposed to address the imbalance of workload and computational power of edge nodes. Experimental results showed that the EC architecture can provide elastic and scalable computing power, and the proposed DIVS system can efficiently handle video surveillance and analysis tasks.