منصة إيفاد العلمية

الدكتورة نهى هاشم

إرسال رسالة

التخصص: دكتوراه هندسة الكترونيك واتصالات

الجامعة: التكنولوجية - العراق

النقاط:

معامل الإنتاج البحثي

الخبرات العلمية

تقييم البحوث الهندسية في مجال هندسة الاتصالات والكترونيك
اعداد البحوث الهندسية في مجال الالكترونيك والاتصالات

الأبحاث المنشورة

A novel real-time multiple objects detection and tracking framework for different challenges

المجلة: Alexandria Engineering Journal

سنة النشر: 2022

تاريخ النشر: 2022-12-01

Recently, there was a lot of researches on real-time detection and tracking algorithms, as the frequent use of surveillance cameras and the expansion of its applications, especially in security and surveillance. However, many challenges have emerged that hinder monitoring systems' work, whether in the detection or tracking stage. We propose a robust new algorithm to detect and track objects from natural scenes captured with real-time cameras to achieve this. This work aims to create a detection and tracking algorithm that is responsive to actual and fundamental changes. This algorithm is characterized by the detection of multiple moving creatures, limited resources, and different challenges. This algorithm combines principal component analysis and deep learning networks to make the most of these two approaches' advantages to achieve an intelligent detection and tracking system that works in real-time. It is done adaptively between the two approaches to enhance performance compared to the existing detection and tracking algorithms. The experimental results showed the new algorithm's effectiveness and efficiency by comparing it with other detection and tracking systems and obtaining good

REAL-TIME MOVING OBJECTS DETECTION AND TRACKING USING DEEP-STREAM TECHNOLOGY

المجلة: Journal of Engineering Science and Technology

سنة النشر: 2021

تاريخ النشر: 2021-02-01

Recently, the deep learning strategy has outperformed the rest of the algorithms related to object detection and tracking in terms of performance and efficiency. It has demonstrated the adaptation of many scientific and practical applications to artificial intelligence (AI) systems. The aim of this paper is primarily to build and develop a real-time object detection and tracking algorithm embedded in an AI computing device known as Nvidia Jetson TX2. It improves the performance of the proposed algorithm. It brings its work closer to reality. DeepStream Software Development Kit (DS-SDK) was used to achieve high performance and interact with multiple video sources at the same time as well. Many convolutional neural networks were used inside the proposed algorithm, such as those based on Fast Region-Convolution Neural Network (Fast R-CNN), Single Shot Detector (SSD), and You Only Look Once (YOLO) network. Its performance in treating different video clips with deep streams of piping compared. The experimental results showed the excellent accuracy and speed of the proposed algorithm in achieving the desired goal.

Objects detection and tracking using fast principle component purist and kalman filter

المجلة: : International Journal of Electrical and Computer Engineering (IJECE)

سنة النشر: 2020

تاريخ النشر: 2020-04-01

The detection and tracking of moving objects attracted a lot of concern because of the vast computer vision applications. This paper proposes a new algorithm based on several methods for identifying, detecting, and tracking an object in order to develop an effective and efficient system in several applications. This algorithm has three main parts: the first part for background modeling and foreground extraction, the second part for smoothing, filtering and detecting moving objects within the video frame and the last part includes tracking and prediction of detected objects. In this proposed work, a new algorithm to detect moving objects from video data is designed by the Fast Principle Component Purist (FPCP). Then we used an optimal filter that performs well to reduce noise through the median filter. The Fast Region convolution neural networks (Fast- RCNN) is used to add smoothness to the spatial identification of objects and their areas. Then the detected object is tracked by Kalman Filter. Experimental results show that our algorithm adapts to different situations and outperforms many existing algorithms

Real-time object detection with simultaneous denoising using low-rank and total variation models

المجلة: International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA)

سنة النشر: 2020

تاريخ النشر: 2020-06-28

The detection of objects in video scenes is the most prominent research topic in computer vision. It is the result of a wide variety of applications, such as virtual reality and intelligent surveillance systems, so that the system is based mainly on the detection of objects or moving objects. Due to the great success of the Foreground/Background Separation algorithms by decomposing the low-order matrix recently, we propose a new real-time incremental algorithm based on Low Rank and Total Variation (TV) model while simultaneously eliminating various noise. In this research, the proposed algorithms applied to solve multiple problems, such as Dynamic Background, Variation Background, with time. Also, we used real or online videos that will allow the adaptive modeling method to automatically remove noise and detect foreground (or intruder) on such scenes. The background modeling challenges in videos do not involve environmental differences such as lighting or weather changes. To check the effectiveness and efficiency of the proposed algorithms, we have experimented with real-time videos. Analytical experiments and results show the ability and efficiency of our method as well as the low computational cost of our proposed algorithms.

Enhancement Performance of Multiple Objects Detection and Tracking for Real time and Online Applications

المجلة: International Journal of Intelligent Engineering and Systems

سنة النشر: 2020

تاريخ النشر: 2020-09-17

Multi-object detection and tracking systems represent one of the basic and important tasks of surveillance and video traffic systems. Recently. The proposed tracking algorithms focused on the detection mechanism. It showed significant improvement in performance in the field of computer vision. Though. It faced many challenges and problems, such as many blockages and segmentation of paths, in addition to the increasing number of identification keys and false-positive paths. In this work, an algorithm was proposed that integrates information on appearance and visibility features to improve the tracker's performance. It enables us to track multiple objects throughout the video and for a longer period of clogging and buffer a number of ID switches. An effective and accurate data set, tools, and metrics were also used to measure the efficiency of the proposed algorithm. The experimental results show the great improvement in the performance of the tracker, with high accuracy of more than 65%, which achieves competitive performance with the existing algorithms

Automatic Objects Detection and Tracking Using FPCP, Blob Analysis and Kalman Filter

المجلة: Engineering and Technology Journal

سنة النشر: 2020

تاريخ النشر: 2020-02-25

Object detection and tracking are key mission in computer visibility applications, including civil or military surveillance systems. However, there are major challenges that have an effective role in the accuracy of detection and tracking such as the ability of the system to track the target and the response speed of the system in different environments as well as the presence of noise in the captured video sequence. In this proposed work, a new algorithm to detect moving objects from video data is designed by the Fast Principle Component Purist (FPCP). Then, we used an ideal filter that performs well to reduce noise through the morphological filter. The Blob analysis is used to add smoothness to the spatial identification of objects and their areas, and finally, the detected object is tracked by Kalman Filter. The applied examples demonstrated the efficiency and capability of the proposed system for noise removal, detection accuracy and trackin