EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices

• URL: http://arxiv.org/abs/2308.08717v1
• Date: Thu, 17 Aug 2023 00:49:44 GMT
• Title: EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices
• Authors: Liang Wang, Nan Zhang, Xiaoyang Qu, Jianzong Wang, Jiguang Wan, Guokuan Li, Kaiyu Hu, Guilin Jiang, Jing Xiao
• Abstract summary: EdgeMA is a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time. We have incorporated a method of model adaptation based on importance weighting, specifically designed to update models to cope with the label distribution shift.
• Score: 31.01270257565127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time video analytics on edge devices for changing scenes remains a difficult task. As edge devices are usually resource-constrained, edge deep neural networks (DNNs) have fewer weights and shallower architectures than general DNNs. As a result, they only perform well in limited scenarios and are sensitive to data drift. In this paper, we introduce EdgeMA, a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time, addressing the data drift problem. EdgeMA extracts the gray level co-occurrence matrix based statistical texture feature and uses the Random Forest classifier to detect the domain shift. Moreover, we have incorporated a method of model adaptation based on importance weighting, specifically designed to update models to cope with the label distribution shift. Through rigorous evaluation of EdgeMA on a real-world dataset, our results illustrate that EdgeMA significantly improves inference accuracy.

Related papers

• Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations [49.173541207550485]
  Adaptive Meshing By Expert Reconstruction (AMBER) is an imitation learning problem. AMBER combines a graph neural network with an online data acquisition scheme to predict the projected sizing field of an expert mesh. We experimentally validate AMBER on 2D meshes and 3D meshes provided by a human expert, closely matching the provided demonstrations and outperforming a single-step CNN baseline.
  arXiv  Detail & Related papers  (2024-06-20T10:01:22Z)
• Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation [56.79064699832383]
  We establish a Cloud-Edge Elastic Model Adaptation (CEMA) paradigm in which the edge models only need to perform forward propagation. In our CEMA, to reduce the communication burden, we devise two criteria to exclude unnecessary samples from uploading to the cloud.
  arXiv  Detail & Related papers  (2024-02-27T08:47:19Z)
• DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data [14.434647668734184]
  We propose DOMINO, a novel HDC learning framework addressing the distribution shift problem in noisy multi-sensor time-series data. DOMINO achieves on average 2.04% higher accuracy than state-of-the-art (SOTA) DNN-based domain generalization techniques, and delivers 16.34x faster training and 2.89x faster inference.
  arXiv  Detail & Related papers  (2023-08-07T04:44:12Z)
• MSECNet: Accurate and Robust Normal Estimation for 3D Point Clouds by Multi-Scale Edge Conditioning [8.177876899141486]
  Estimating surface normals from 3D point clouds is critical for various applications, including surface reconstruction and rendering. We propose a novel approach called MSECNet, which improves estimation in normal varying regions by treating normal variation modeling as an edge detection problem.
  arXiv  Detail & Related papers  (2023-08-04T10:26:59Z)
• Video Mask Transfiner for High-Quality Video Instance Segmentation [102.50936366583106]
  Video Mask Transfiner (VMT) is capable of leveraging fine-grained high-resolution features thanks to a highly efficient video transformer structure. Based on our VMT architecture, we design an automated annotation refinement approach by iterative training and self-correction. We compare VMT with the most recent state-of-the-art methods on the HQ-YTVIS, as well as the Youtube-VIS, OVIS and BDD100K MOTS.
  arXiv  Detail & Related papers  (2022-07-28T11:13:37Z)
• Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping [116.26579152942162]
  Graph neural networks (GNNs) are processing architectures that exploit graph structural information to model representations from network data. Despite their success, GNNs suffer from sub-optimal generalization performance given limited training data. This paper proposes Topology Adaptive Edge Dropping to improve generalization performance and learn robust GNN models.
  arXiv  Detail & Related papers  (2021-06-05T13:20:36Z)
• Towards Unsupervised Fine-Tuning for Edge Video Analytics [1.1091582432763736]
  We propose a method for improving accuracy of edge models without any extra compute cost by means of automatic model specialization. Results show that our method can automatically improve accuracy of pre-trained models by an average of 21%.
  arXiv  Detail & Related papers  (2021-04-14T12:57:40Z)
• LevelSet R-CNN: A Deep Variational Method for Instance Segmentation [79.20048372891935]
  Currently, many state of the art models are based on the Mask R-CNN framework. We propose LevelSet R-CNN, which combines the best of both worlds by obtaining powerful feature representations. We demonstrate the effectiveness of our approach on COCO and Cityscapes datasets.
  arXiv  Detail & Related papers  (2020-07-30T17:52:18Z)
• Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing [65.78881372074983]
  IoT devices can hardly afford complex deep neural networks (DNN) models, and offloading anomaly detection tasks to the cloud incurs long delay. We propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems. We show that our proposed approach significantly reduces detection delay without sacrificing accuracy, as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-04-15T06:13:33Z)

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.