scholarly journals A Graphical Approach to Testing Real-Time Embedded Devices

2009 ◽  
Author(s):  
Steven M Day
Keyword(s):  
Real Time ◽  
Embedded Devices ◽  
Graphical Approach

scholarly journals Real-Time Instance Segmentation of Traffic Videos for Embedded Devices

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 275
Author(s):  
Ruben Panero Martinez ◽  
Ionut Schiopu ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Training Procedure ◽  
Segmentation Method ◽  
Embedded Devices ◽  
Network Training ◽  
Assignment Algorithm ◽  
Ablation Study ◽  
Reduced Rate ◽  
Instance Segmentation

The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with 31.57 average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.

scholarly journals A Streaming Cloud Platform for Real-Time Video Processing on Embedded Devices

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Weishan Zhang ◽  
Haoyun Sun ◽  
Dehai Zhao ◽  
Liang Xu ◽  
Xin Liu ◽  
...  
Keyword(s):  
Real Time ◽  
Video Processing ◽  
Cloud Platform ◽  
Embedded Devices

scholarly journals A Lightweight Object Detection Network for Real-Time Detection of Driver Handheld Call on Embedded Devices

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Zuopeng Zhao ◽  
Zhongxin Zhang ◽  
Xinzheng Xu ◽  
Yi Xu ◽  
Hualin Yan ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Recognition Accuracy ◽  
Detection Algorithm ◽  
Experimental Results ◽  
Identification Accuracy ◽  
Convolution Kernel ◽  
Small Target ◽  
Resource Constrained ◽  
Embedded Devices

It is necessary to improve the performance of the object detection algorithm in resource-constrained embedded devices by lightweight improvement. In order to further improve the recognition accuracy of the algorithm for small target objects, this paper integrates 5 × 5 deep detachable convolution kernel on the basis of MobileNetV2-SSDLite model, extracts features of two special convolutional layers in addition to detecting the target, and designs a new lightweight object detection network—Lightweight Microscopic Detection Network (LMS-DN). The network can be implemented on embedded devices such as NVIDIA Jetson TX2. The experimental results show that LMS-DN only needs fewer parameters and calculation costs to obtain higher identification accuracy and stronger anti-interference than other popular object detection models.

scholarly journals Real time implementation of embedded devices as a security system in intelligent vehicles connected via Vanets

2019 ◽  
Vol 9 (6) ◽  
pp. 4788
Author(s):  
Senthilnathan Palaniapan ◽  
Mohammed Ahsan Kollathodi
Keyword(s):  
Real Time ◽  
Security System ◽  
Intelligent Vehicles ◽  
Motion Sensors ◽  
Embedded Devices ◽  
The Road ◽  
Monitoring Reliability ◽  
Peripheral Surface ◽  
Computer Based ◽  
Safety Features

<p>The fast boom of  technology has made our lives easier. The number of computer based functions embedded in cars have multiplied extensively over the past two decades. These days, many embedded sensors allowing localization and verbal exchange are being advanced to enhance reliability, protection and define new exploitation modes in intelligent guided transports. An in-car embedded electronic architecture is a complex setup machine, the improvement of that particular system is related to unique manufacturers and providers. There are several factors required in an efficient and secure system along with protection features, real time monitoring, reliability, robustness, and many other integrated features[1-2]. The appearance of modern era has also expanded the use of vehicles and  its associated dangers. Dangers and the road accidents take place often which causes loss of lives and assets due to the bad emergency centres, lack of safety features and limitations within devices embedded within a vehicle.  A rpm-speed calculating device can be used in a vehicle such that risku situations while driving can be detected. A system with Ultra sonic sensor can be used as a crash detector of the automobile in the course of the event and also after a crash. With indicators from the device,  extreme coincidences also can be recognized. .As the amount of urban automobile grows automobile theft has become a shared difficulty for all citizens. As a solution an antitheft system can be implemented using PIR motion sensors where the system can be attached to the peripheral surface of the vehicle. When these sensors are interfaced with  Arduino microcontroller an efficient and reliable security system can be developed[3].</p>

scholarly journals Drowsiness Classification for Internal Driving Situation Awareness on Mobile Platform

2021 ◽  
Vol 8 (2) ◽  
pp. 3-7
Author(s):  
Julkar Nine ◽  
Naeem Ahmed ◽  
Rahul Mathavan
Keyword(s):  
Real Time ◽  
Pose Estimation ◽  
Situation Awareness ◽  
Automobile Industry ◽  
Identification Accuracy ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Embedded Devices ◽  
Falling Asleep ◽  
Technical Solutions

the sleeping driver is potentially more likely to cause an accident than the person who speeds up since the driver is the victim of sleepiness. Automobile industry researchers, including manufacturers, seek to solve this issue with various technical solutions that can avoid such a situation. This paper proposes an implementation of a lightweight method to detect driver's sleepiness using facial landmarks and head pose estimation based on neural network methodologies on a mobile device. We try to improve the accurateness by using face images that the camera detects and passes to CNN to identify sleepiness. Firstly, applied a behavioral landmark's sleepiness detection process. Then, an integrated Head Pose Estimation technique will strengthen the system's reliability. The preliminary findings of the tests demonstrate that with real-time capability, more than 86% identification accuracy can be reached in several real-world scenarios for all classes, including with glasses, without glasses, and light-dark background. This work aims to classify drowsiness, warn, and inform drivers, helping them to stop falling asleep at the wheel. The integrated CNN-based method is used to create a high accuracy and simple-to-use real-time driver drowsiness monitoring framework for embedded devices and Android phones

scholarly journals Evaluation of Classical Machine Learning Techniques towards Urban Sound Recognitionon Embedded Systems

2019 ◽  
Vol 9 (18) ◽  
pp. 3885 ◽  
Author(s):  
Bruno da Silva ◽  
Axel W. Happi ◽  
An Braeken ◽  
Abdellah Touhafi
Keyword(s):  
Machine Learning ◽  
Embedded Systems ◽  
Real Time ◽  
Urban Environments ◽  
Machine Learning Techniques ◽  
Embedded Devices ◽  
Sound Classification ◽  
Learning Techniques ◽  
Urban Monitoring ◽  
Constrained Devices

Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real-time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real-time urban audio recognition. Using such devices for the edge computation when acting as nodes of Wireless Sensor Networks (WSN) drastically alleviates the required bandwidth consumption. In this paper, we evaluate classical Machine Learning (ML) techniques for urban sound classification on embedded devices with respect to accuracy and execution time. This evaluation provides a real estimation of what can be expected when performing urban sound classification on such constrained devices. In addition, a cascade approach is also proposed to combine ML techniques by exploiting embedded characteristics such as pipeline or multi-thread execution present in current embedded devices. The accuracy of this approach is similar to the traditional solutions, but provides in addition more flexibility to prioritize accuracy or timing.

scholarly journals Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video

2017 ◽  
Vol 3 (1) ◽  
Author(s):  
Mohammad Javad Shaifee ◽  
Brendan Chywl ◽  
Francis Li ◽  
Alexander Wong
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Network Architecture ◽  
Object Localization ◽  
Embedded Computing ◽  
Embedded Devices ◽  
Limited Memory ◽  
Adaptive Inference ◽  
Speed And Accuracy ◽  
New Framework

Object detection is considered one of the most challenging problemsin this field of computer vision, as it involves the combinationof object classification and object localization within a scene. Recently,deep neural networks (DNNs) have been demonstrated toachieve superior object detection performance compared to otherapproaches, with YOLOv2 (an improved You Only Look Once model)being one of the state-of-the-art in DNN-based object detectionmethods in terms of both speed and accuracy. Although YOLOv2can achieve real-time performance on a powerful GPU, it still remainsvery challenging for leveraging this approach for real-timeobject detection in video on embedded computing devices withlimited computational power and limited memory. In this paper,we propose a new framework called Fast YOLO, a fast You OnlyLook Once framework which accelerates YOLOv2 to be able toperform object detection in video on embedded devices in a realtimemanner. First, we leverage the evolutionary deep intelligenceframework to evolve the YOLOv2 network architecture and producean optimized architecture (referred to as O-YOLOv2 here) that has2.8X fewer parameters with just a 2% IOU drop. To further reducepower consumption on embedded devices while maintaining performance,a motion-adaptive inference method is introduced intothe proposed Fast YOLO framework to reduce the frequency ofdeep inference with O-YOLOv2 based on temporal motion characteristics.Experimental results show that the proposed Fast YOLOframework can reduce the number of deep inferences by an averageof 38.13%, and an average speedup of 3.3X for objectiondetection in video compared to the original YOLOv2, leading FastYOLO to run an average of 18FPS on a Nvidia Jetson TX1 embeddedsystem.

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