A Hardware-Software Co-Design for Object Detection Using High-Level Synthesis Tools

2019 ◽  
Vol 8 (1) ◽  
pp. 63-73
Author(s):  
Aiman Badawi ◽  
Muhammad Bilal
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Video Processing ◽  
Modular Design ◽  
High Level Synthesis ◽  
Unified Framework ◽  
Daunting Task ◽  
High Level ◽  
Rapid Deployment

Object detection is a vital component of modern video processing systems, and despite the availability of several efficient open-source feature-classifier frameworks and their corresponding implementation schemes, inclusion of this feature as a drop-in module in larger computer vision systems is still considered a daunting task. To this end, this work describes an open-source unified framework which can be used to train, test, and deploy an SVM-based object detector as a hardware-software co-design on FPGA using Simulink high-level synthesis tool. The proposed modular design can be seamlessly integrated within full systems developed using Simulink Computer Vision toolbox for rapid deployment. FPGA synthesis results show that the proposed hardware architecture utilizes fewer logic resources than the contemporary designs for similar operation. Moreover, experimental evidence has been provided to prove the generalization of the framework in efficiently detecting a variety of objects of interest including pedestrians, faces and traffic signs.

ON METHODS OF OBJECT DETECTION IN VIDEO STREAMS

2017 ◽  
Vol 2 (1) ◽  
pp. 80-87
Author(s):  
Puyda V. ◽  
◽  
Stoian. A.
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Feature Detection ◽  
Video Stream ◽  
Object Identification ◽  
Vision Systems ◽  
Modern Computer ◽  
Computer Vision Systems ◽  
Open Source Hardware

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed. In this paper, we study three algorithms which can be used to detect objects of different nature and are based on different approaches: detection of color non-uniformities, frame difference and feature detection. As the input data, we use a video stream which is obtained from a video camera or from an mp4 video file. Simulations and testing of the algoritms were done on a universal computer based on an open-source hardware, built on the Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC processor with frequency 1,5GHz. The software was created in Visual Studio 2019 using OpenCV 4 on Windows 10 and on a universal computer operated under Linux (Raspbian Buster OS) for an open-source hardware. In the paper, the methods under consideration are compared. The results of the paper can be used in research and development of modern computer vision systems used for different purposes. Keywords: object detection, feature points, keypoints, ORB detector, computer vision, motion detection, HSV model color

Accelerating Sobel Edge Detection Using Compressor Cells Over FPGAs

2018 ◽  
pp. 1133-1154
Author(s):  
Ahmed Abouelfarag ◽  
Marwa Ali Elshenawy ◽  
Esraa Alaaeldin Khattab
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
Video Processing ◽  
High Level Synthesis ◽  
Computational Time ◽  
The Novel ◽  
Image And Video Processing ◽  
Sobel Edge Detection ◽  
High Level ◽  
Time Image

Recently, computer vision is playing an important role in many essential human-computer interactive applications, these applications are subject to a “real-time” constraint, and therefore it requires a fast and reliable computational system. Edge Detection is the most used approach for segmenting images based on changes in intensity. There are various kernels used to perform edge detection, such as: Sobel, Robert, and Prewitt, upon which, the most commonly used is Sobel. In this research a novel type of operator cells that perform addition is introduced to achieve computational acceleration. The novel operator cells have been employed in the chosen FPGA Zedboard which is well-suited for real-time image and video processing. Accelerating the Sobel edge detection technique is exploited using different tools such as the High-Level Synthesis tools provided by Vivado. This enhancement shows a significant improvement as it decreases the computational time by 26% compared to the conventional adder cells.

Accelerating Sobel Edge Detection Using Compressor Cells Over FPGAs

2017 ◽  
pp. 1-21
Author(s):  
Ahmed Abouelfarag ◽  
Marwa Ali Elshenawy ◽  
Esraa Alaaeldin Khattab
Keyword(s):  
Edge Detection ◽  
Real Time ◽  
Video Processing ◽  
High Level Synthesis ◽  
Computational Time ◽  
The Novel ◽  
Image And Video Processing ◽  
Sobel Edge Detection ◽  
High Level ◽  
Time Image

Recently, computer vision is playing an important role in many essential human-computer interactive applications, these applications are subject to a “real-time” constraint, and therefore it requires a fast and reliable computational system. Edge Detection is the most used approach for segmenting images based on changes in intensity. There are various kernels used to perform edge detection, such as: Sobel, Robert, and Prewitt, upon which, the most commonly used is Sobel. In this research a novel type of operator cells that perform addition is introduced to achieve computational acceleration. The novel operator cells have been employed in the chosen FPGA Zedboard which is well-suited for real-time image and video processing. Accelerating the Sobel edge detection technique is exploited using different tools such as the High-Level Synthesis tools provided by Vivado. This enhancement shows a significant improvement as it decreases the computational time by 26% compared to the conventional adder cells.

Weapon and Object Detection Using Mobile-Net SSD Model in Deep Neural Network

2021 ◽  
Vol 9 (8) ◽  
pp. 1573-1582
Author(s):  
S Gopi Naik
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Optimization Algorithms ◽  
Object Identification ◽  
Integrated System ◽  
Raspberry Pi ◽  
High Level

Abstract: The plan is to establish an integrated system that can manage high-quality visual information and also detect weapons quickly and efficiently. It is obtained by integrating ARM-based computer vision and optimization algorithms with deep neural networks able to detect the presence of a threat. The whole system is connected to a Raspberry Pi module, which will capture live broadcasting and evaluate it using a deep convolutional neural network. Due to the intimate interaction between object identification and video and image analysis in real-time objects, By generating sophisticated ensembles that incorporate various low-level picture features with high-level information from object detection and scenario classifiers, their performance can quickly plateau. Deep learning models, which can learn semantic, high-level, deeper features, have been developed to overcome the issues that are present in optimization algorithms. It presents a review of deep learning based object detection frameworks that use Convolutional Neural Network layers for better understanding of object detection. The Mobile-Net SSD model behaves differently in network design, training methods, and optimization functions, among other things. The crime rate in suspicious areas has been reduced as a consequence of weapon detection. However, security is always a major concern in human life. The Raspberry Pi module, or computer vision, has been extensively used in the detection and monitoring of weapons. Due to the growing rate of human safety protection, privacy and the integration of live broadcasting systems which can detect and analyse images, suspicious areas are becoming indispensable in intelligence. This process uses a Mobile-Net SSD algorithm to achieve automatic weapons and object detection. Keywords: Computer Vision, Weapon and Object Detection, Raspberry Pi Camera, RTSP, SMTP, Mobile-Net SSD, CNN, Artificial Intelligence.

scholarly journals FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Robert Oostenveld ◽  
Pascal Fries ◽  
Eric Maris ◽  
Jan-Mathijs Schoffelen
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Modular Design ◽  
Large Data ◽  
Data Sets ◽  
Time Frequency ◽  
Electrophysiological Data ◽  
Open Source Software Package ◽  
Advanced Analysis ◽  
High Level

This paper describes FieldTrip, an open source software package that we developed for the analysis of MEG, EEG, and other electrophysiological data. The software is implemented as a MATLAB toolbox and includes a complete set of consistent and user-friendly high-level functions that allow experimental neuroscientists to analyze experimental data. It includes algorithms for simple and advanced analysis, such as time-frequency analysis using multitapers, source reconstruction using dipoles, distributed sources and beamformers, connectivity analysis, and nonparametric statistical permutation tests at the channel and source level. The implementation as toolbox allows the user to perform elaborate and structured analyses of large data sets using the MATLAB command line and batch scripting. Furthermore, users and developers can easily extend the functionality and implement new algorithms. The modular design facilitates the reuse in other software packages.

scholarly journals Saliency Object Detection from Video Streams using Salient-Graph Model with High-Level Background Prior

2019 ◽  
Vol 8 (6) ◽  
pp. 3021-3025
Keyword(s):  
Object Detection ◽  
Video Segmentation ◽  
False Positive Rate ◽  
Visible Region ◽  
Graph Model ◽  
Saliency Map ◽  
Computational Time ◽  
Daunting Task ◽  
High Level ◽  
Background Prior

This study proposes a novel salient graph model with high-level background prior. As usual, the collected data is pre-processed and then used for segmentation analysis. Object detection is still a daunting task due to increased complexity of false positive rate. Thus, a salient graph model is constructed using high-level background prior. Initially, the contrast of an image enhanced for superpixels and used for finding the shortest path of visible region. Then, saliency map is formed by spatial analysis of those visible superpixels. In salient post-processing, the salient graph is constructed by labelling background nodes with minimized cost. Based on formed salient region, each adjacent superpixel with background nodes are used for queries. Atlast, the estimated saliency and objectness measures detects the objects with minimal constraints. The proposed framework is analyzed on SegTrack and SegTrack 2, video segmentation dataset. The results states that the proposed method achieves better results than state of the art models by improved precision, recall, F-measure and computational time.

scholarly journals Accuracy and Efficiency Comparison of Object Detection Open-Source Models

2021 ◽  
Vol 17 (05) ◽  
pp. 165
Author(s):  
Brahim Jabir ◽  
Noureddine Falih ◽  
Khalid Rahmani
Keyword(s):  
Computer Vision ◽  
Object Detection ◽  
Open Source ◽  
Large Scale ◽  
Performance Comparison ◽  
Yield Potential ◽  
Temperate Climate ◽  
Weed Species ◽  
Weed Detection ◽  
Cloudy Weather

In agriculture, weeds cause direct damage to the crop, and it primarily affects the crop yield potential. Manual and mechanical weeding methods consume a lot of energy and time and do not give efficient results. Chemical weed control is still the best way to control weeds. However, the widespread and large-scale use of herbicides is harmful to the environment. Our study's objective is to propose an efficient model for a smart system to detect weeds in crops in real-time using computer vision. Our experiment dataset contains images of two different weed species well known in our region strained in this region with a temperate climate. The first is the Phalaris Paradoxa. The second is Convolvulus, manually captured with a professional camera from fields under different lighting conditions (from morning to afternoon in sunny and cloudy weather). The detection of weed and crop has experimented with four recent pre-configured open-source computer vision models for object detection: Detectron2, EfficientDet, YOLO, and Faster R-CNN. The performance comparison of weed detection models is executed on the Open CV and Keras platform using python language.

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