mlsanityData to 14 Oct 2026

VID-WIN: Fast Video Event Matching with Query-Aware Windowing at the Edge for the Internet of Multimedia Things

arXiv 2105.02957 ↗First posted 27 Apr 2021cs.CV cs.DC cs.MMcited by 0

Not on the map. Why?

Abstract (arXiv)

Efficient video processing is a critical component in many IoMT applications to detect events of interest. Presently, many window optimization techniques have been proposed in event processing with an underlying assumption that the incoming stream has a structured data model. Videos are highly complex due to the lack of any underlying structured data model. Video stream sources such as CCTV cameras and smartphones are resource-constrained edge nodes. At the same time, video content extraction is expensive and requires computationally intensive Deep Neural Network (DNN) models that are primarily deployed at high-end (or cloud) nodes. This paper presents VID-WIN, an adaptive 2-stage allied windowing approach to accelerate video event analytics in an edge-cloud paradigm. VID-WIN runs parallelly across edge and cloud nodes and performs the query and resource-aware optimization for state-based complex event matching. VID-WIN exploits the video content and DNN input knobs to accelerate the video inference process across nodes. The paper proposes a novel content-driven micro-batch resizing, queryaware caching and micro-batch based utility filtering strategy of video frames under resource-constrained edge nodes to improve the overall system throughput, latency, and network usage. Extensive evaluations are performed over five real-world datasets. The experimental results show that VID-WIN video event matching achieves ~2.3X higher throughput with minimal latency and ~99% bandwidth reduction compared to other baselines while maintaining query-level accuracy and resource bounds.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 95 references that are in the map. First posted on arXiv 27 Apr 2021.

Builds on

18 of 95 references.

References · 18

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  4. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  5. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  6. 2018Focus: Querying Large Video Datasets with Low Latency and Low Costcited by 46
  7. 2018BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analyticscited by 41
  8. 2019Scaling Video Analytics on Constrained Edge Nodescited by 36
  9. 2018Physical Representation-based Predicate Optimization for a Visual Analytics Databasecited by 19
  10. 2016iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environmentscited by 11
  11. 2020VidCEP: Complex Event Processing Framework to Detect Spatiotemporal Patterns in Video Streamscited by 5
  12. 2018Precision and Recall for Range-Based Anomaly Detectioncited by 3
6 more
  1. 2020eSPICE: Probabilistic Load Shedding from Input Event Streams in Complex Event Processingcited by 3
  2. 2020pSPICE: Partial Match Shedding for Complex Event Processingcited by 2
  3. 2020hSPICE: State-Aware Event Shedding in Complex Event Processingcited by 2
  4. 2017Foundations of Complex Event Processingcited by 1
  5. 2020Knowledge Graph Driven Approach to Represent Video Streams for Spatiotemporal Event Pattern Matching in Complex Event Processingcited by 1
  6. 2020Traffic Prediction Framework for OpenStreetMap using Deep Learning based Complex Event Processing and Open Traffic Camerascited by 1

Built on by

Not cited yet.