Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers

• URL: http://arxiv.org/abs/2209.15148v1
• Date: Fri, 30 Sep 2022 00:22:57 GMT
• Title: Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers
• Authors: Minjeong Kim, Jimin Koo
• Abstract summary: We propose an embedded system that can process Ghoddoosian's drowsiness detection algorithm on a small minicomputer. We use the AioRTC protocol on GitHub to conduct real-time transmission of video frames from the client to the server.
• Score: 2.314847093228566
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of solutions implementing machine learning techniques have been proposed by researchers. Among existing methods, Ghoddoosian et al.'s drowsiness detection method utilizes temporal blinking patterns to detect early signs of drowsiness. Although the method reported promising results, Ghoddoosian et al.'s algorithm was developed and tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an embedded system that can process Ghoddoosian's drowsiness detection algorithm on a small minicomputer and interact with the user by phone; combined, the devices are powerful enough to run a web server and our drowsiness detection server. We used the AioRTC protocol on GitHub to conduct real-time transmission of video frames from the client to the server and evaluated the communication speed and processing times of the program on various platforms. Based on our results, we found that a Mini PC was most suitable for our proposed system. Furthermore, we proposed an algorithm that considers the importance of sensitivity over specificity, specifically regarding drowsiness detection algorithms. Our algorithm optimizes the threshold to adjust the false positive and false negative rates of the drowsiness detection models. We anticipate our proposed platform can help many researchers to advance their research on drowsiness detection solutions in embedded system settings.

Related papers

• VigilEye -- Artificial Intelligence-based Real-time Driver Drowsiness Detection [0.5549794481031468]
  This study presents a novel driver drowsiness detection system that combines deep learning techniques with the OpenCV framework. The system uses facial landmarks extracted from the driver's face as input to Convolutional Neural Networks trained to recognise drowsiness patterns. The proposed system has the potential to enhance road safety by providing timely alerts to prevent accidents caused by driver fatigue.
  arXiv  Detail & Related papers  (2024-06-21T20:53:49Z)
• RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture [24.824596231020585]
  We propose a packet-level network intrusion detection solution that makes use of Recurrent Autoencoders to integrate an arbitrary-length sequence of packets into a more compact joint feature embedding. We show that our approach leads to an extremely efficient, real-time solution with high detection accuracy at the packet level.
  arXiv  Detail & Related papers  (2023-11-27T17:30:19Z)
• Online Learning of Wheel Odometry Correction for Mobile Robots with Attention-based Neural Network [63.8376359764052]
  Modern robotic platforms need a reliable localization system to operate daily beside humans. Simple pose estimation algorithms based on filtered wheel and inertial odometry often fail in the presence of abrupt kinematic changes and wheel slips. We propose an innovative online learning approach for wheel odometry correction, paving the way for a robust multi-source localization system.
  arXiv  Detail & Related papers  (2023-03-21T10:30:31Z)
• Towards AIOps in Edge Computing Environments [60.27785717687999]
  This paper describes the system design of an AIOps platform which is applicable in heterogeneous, distributed environments. It is feasible to collect metrics with a high frequency and simultaneously run specific anomaly detection algorithms directly on edge devices.
  arXiv  Detail & Related papers  (2021-02-12T09:33:00Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Artificial Intelligence Enabled Traffic Monitoring System [3.085453921856008]
  This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs.
  arXiv  Detail & Related papers  (2020-10-02T22:28:02Z)
• Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios [0.0]
  We present an efficient framework to solve the license plate detection and recognition tasks simultaneously. It is a lightweight and unified deep neural network, that can be optimized end-to-end and work in real-time. Experimental results indicate that the proposed method significantly outperforms the previous state-of-the-art methods in both speed and precision.
  arXiv  Detail & Related papers  (2020-08-25T09:51:33Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning [72.99415402575886]
  Outlier detection is an important data mining task with numerous practical applications. We propose AutoOD, an automated outlier detection framework, which aims to search for an optimal neural network model. Experimental results on various real-world benchmark datasets demonstrate that the deep model identified by AutoOD achieves the best performance.
  arXiv  Detail & Related papers  (2020-06-19T18:57:51Z)
• An Intelligent and Time-Efficient DDoS Identification Framework for Real-Time Enterprise Networks SAD-F: Spark Based Anomaly Detection Framework [0.5811502603310248]
  We will be exploring security analytic techniques for DDoS anomaly detection using different machine learning techniques. In this paper, we are proposing a novel approach which deals with real traffic as input to the system. We study and compare the performance factor of our proposed framework on three different testbeds.
  arXiv  Detail & Related papers  (2020-01-21T06:05:48Z)
• Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
  We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem. We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection. We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
  arXiv  Detail & Related papers  (2020-01-10T05:29:17Z)

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.