Employing of Object Tracking System in Public Surveillance Cameras to Enforce Quarantine and Social Distancing Using Parallel Machine Learning Techniques

Al-Qatawneh, Sokyna M.; Jaber, Khalid Mohammad; Salah, Mosa; Yehia, Dalal; Alqatawneh, Omayma; Abulahoum, Abdulrahman

doi:10.15849/ijasca.211128.12

Cited by 3 publications

(4 citation statements)

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“…Concluding the literature review, artificial intelligence (AI) and recent deep learning techniques could be applied to many areas that facilitate human lives [67,68]. Furthermore, it could be applied in medical applications [69,70], recommender systems [71], job-seeking [72], smart cities and localization [73], hospitals [74,75], object tracking [76][77][78], software engineering [79,80], E-commerce [81], emotional analysis [82], agriculture applications [83,84], and many others [85].…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

et al. 2022

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Emotional intelligence is the automatic detection of human emotions using various intelligent methods. Several studies have been conducted on emotional intelligence, and only a few have been adopted in education. Detecting student emotions can significantly increase productivity and improve the education process. This paper proposes a new deep learning method to detect student emotions. The main aim of this paper is to map the relationship between teaching practices and student learning based on emotional impact. Facial recognition algorithms extract helpful information from online platforms as image classification techniques are applied to detect the emotions of student and/or teacher faces. As part of this work, two deep learning models are compared according to their performance. Promising results are achieved using both techniques, as presented in the Experimental Results Section. For validation of the proposed system, an online course with students is used; the findings suggest that this technique operates well. Based on emotional analysis, several deep learning techniques are applied to train and test the emotion classification process. Transfer learning for a pre-trained deep neural network is used as well to increase the accuracy of the emotion classification stage. The obtained results show that the performance of the proposed method is promising using both techniques, as presented in the Experimental Results Section.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

et al. 2022

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“…One of the most prominent challenges facing systems specialized in discovering and recognizing faces, and it constitutes a challenge that still constitutes a research material in this field is the factors affecting face images such as pose variation, different locations, illumination, expression variation and partially occluded faces as mentioned in [13]. The research for resolving such problems was based in using several approaches that use LBP (Local Binary Pattern), Voila Jones and Histogram.…”

Section: Related Workmentioning

confidence: 99%

“…Evaluating efficiency and performances of these algorithms are presented in many studies, where authors in [13] presented a comparative study to review the performance of these three algorithms and focused in their study on False Negative rate [FNR] and True Positive Rate [TPR]. According to their study as well as in [14], Hog face algorithm founded as the most accurate and effective with (92%) accuracy.…”

Section: Related Workmentioning

confidence: 99%

A New approach to Recognize Human Face Under Unconstrained Environment

Rifaee¹,

Rawajbeh²,

AlOkosh³

et al. 2022

ijasca

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Human face is considered as one of the most useful traits in biometrics, and it has been widely used in education, security, military and many other applications. However, in most of currently deployed face recognition systems ideal imaging conditions are assumed; to capture a fully featured images with enough quality to perform the recognition process. As the unmasked face will have a considerable impact on the numbers of new infections in the era of COVID-19 pandemic, a new unconstrained partial facial recognition method must be developed. In this research we proposed a mask detection method based on HOG (Histogram of Gradient) features descriptor and SVM (Support Vector Machine) to determine whether the face is masked or not, the proposed method was tested over 10000 randomly selected images from Masked Face-Net database and was able to correctly classify 98.73% of the tested images. Moreover, and to extract enough features from partially occluded face images, a new geometrical features extraction algorithm based on Contourlet transform was proposed. The method achieved 97.86% recognition accuracy when tested over 4784 correctly masked face images from Masked Face-Net database. Keywords: Facial Recognition, Unconstraint conditions, masked faces, HOG, Support Vector Machine.

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“…In general, many previous researchers have employed artificial intelligence (AI) in real-life applications, including agriculture, business intelligence, transportation, healthcare, social media, smart cities, and many more [3][4][5][6][7][8]. Overall, our study and discus-sion on AI-based deep learning in the right direction can be a reference guide for future research and development in the plant disease applications domain by academics and industry professionals.…”

Section: Introductionmentioning

confidence: 96%

An Intelligent and Precise Agriculture Model in Sustainable Cities Based on Visualized Symptoms

et al. 2023

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Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or damage to these crops. This paper presents an image processing and a deep learning-based automatic approach that classifies the diseases that strike the apple leaves. The proposed system has been tested using over 18,000 images from the Apple Diseases Dataset by PlantVillage, including images of healthy and affected apple leaves. We applied the VGG-16 architecture to a pre-trained unlabeled dataset of plant leave images. Then, we used some other deep learning pre-trained architectures, including Inception-V3, ResNet-50, and VGG-19, to solve the visualization-related problems in computer vision, including object classification. These networks can train the images dataset and compare the achieved results, including accuracy and error rate between those architectures. The preliminary results demonstrate the effectiveness of the proposed Inception V3 and VGG-16 approaches. The obtained results demonstrate that Inception V3 achieves an accuracy of 92.42% with an error rate of 0.3037%, while the VGG-16 network achieves an accuracy of 91.53% with an error rate of 0.4785%. The experiments show that these two deep learning networks can achieve satisfying results under various conditions, including lighting, background scene, camera resolution, size, viewpoint, and scene direction.

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Employing of Object Tracking System in Public Surveillance Cameras to Enforce Quarantine and Social Distancing Using Parallel Machine Learning Techniques

Cited by 3 publications

References 0 publications

A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

A Novel Deep Learning Technique for Detecting Emotional Impact in Online Education

A New approach to Recognize Human Face Under Unconstrained Environment

An Intelligent and Precise Agriculture Model in Sustainable Cities Based on Visualized Symptoms

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