Recognition and Tracking of Objects in a Clustered Remote Scene Environment

Masood, Haris; Zafar, Amad; Ali, Muhammad Umair; Khan, Muhammad Attique; Mashaly, Ahmed S.; Tariq, Usman; Kang, Byeong-Gwon; Nam, Yunyoung

doi:10.32604/cmc.2022.019572

Cited by 10 publications

(5 citation statements)

References 31 publications

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“…The recent developments in the CNN for scene recognition [33], object recognition [34], and action recognition [35] have produced an impressive performance [36]. Tang and Wang [37] introduced in their seminal work a new art of formulating human face sketches based on Eigen transformation.…”

Section: Related Workmentioning

confidence: 99%

A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

Azhar

Sharif

Raza

et al. 2021

Sensors

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The recent development in the area of IoT technologies is likely to be implemented extensively in the next decade. There is a great increase in the crime rate, and the handling officers are responsible for dealing with a broad range of cyber and Internet issues during investigation. IoT technologies are helpful in the identification of suspects, and few technologies are available that use IoT and deep learning together for face sketch synthesis. Convolutional neural networks (CNNs) and other constructs of deep learning have become major tools in recent approaches. A new-found architecture of the neural network is anticipated in this work. It is called Spiral-Net, which is a modified version of U-Net fto perform face sketch synthesis (the phase is known as the compiler network C here). Spiral-Net performs in combination with a pre-trained Vgg-19 network called the feature extractor F. It first identifies the top n matches from viewed sketches to a given photo. F is again used to formulate a feature map based on the cosine distance of a candidate sketch formed by C from the top n matches. A customized CNN configuration (called the discriminator D) then computes loss functions based on differences between the candidate sketch and the feature. Values of these loss functions alternately update C and F. The ensemble of these nets is trained and tested on selected datasets, including CUFS, CUFSF, and a part of the IIT photo–sketch dataset. Results of this modified U-Net are acquired by the legacy NLDA (1998) scheme of face recognition and its newer version, OpenBR (2013), which demonstrate an improvement of 5% compared with the current state of the art in its relevant domain.

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Section: Related Workmentioning

confidence: 99%

A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

Azhar

Sharif

Raza

et al. 2021

Sensors

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“…Remote sensing images of fruit trees in relatively large orchards can be captured via satellite or aerial imaging. In the case of satellite remote sensing, cloudy weather is a major challenge that makes it difficult to detect fruit trees due to the possibly poor quality of the images captured [3,4]. The limitation of the spatial resolution of satellite images is another major challenge for the accurate detection of IFTs.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5

Xiong,

Zeng,

Lai

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The location and number of individual fruit trees (IFTs) are critical for investigations on planting areas, fruit yield predictions, and smart orchard planning and management. These data are conventionally obtained through manual and statistical investigations that require long, laborious, and costly efforts. Object detection models of deep learning could provide an opportunity to detect IFTs accurately, which is essential for rapidly obtaining these data and reducing human operation errors. This study proposed an approach for detecting IFTs and mapping their spatial distributions by integrating deep learning with unmanned aerial vehicle (UAV) remote sensing. UAV remote sensing was used to collect high-resolution images of fruit trees in pomelo orchards in Meizhou, South China. Based on these images, a new individual pomelo tree image sample (IPTIS) dataset was created through manual interpretation and field investigation. The evaluation results revealed that YOLOv5s was the best model among the five YOLOv5 models (i.e., YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x, whose layers, parameters, and floating-point operations all increased with the depth and width of layers.) of different scales considered for optimization. Moreover, the coordinate attention (CA)-optimized YOLOv5 model (YOLOv5s-CA) is the best model (named Manuscript

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“…Over the past few years, artificial intelligence technology has increased in prominence, with applications ranging from image recognition [1] to adversarial assaults [2] to privacy protection [3,4] and everything in between. The gains produced in the discipline of computer vision applying datadriven deep supervised learning, which have created exceptional outcomes in a wide range of vision challenges [5][6][7][8], are especially notable.…”

Section: Introductionmentioning

confidence: 99%

Automated Registration of Multiangle SAR Images Using Artificial Intelligence

Chopra

Gollamandala²,

Ahmed

et al. 2022

Mobile Information Systems

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Traditionally, nonlinear data processing has been approached via the use of polynomial filters, which are straightforward expansions of many linear methods, or through the use of neural network techniques. In contrast to linear approaches, which often provide algorithms that are simple to apply, nonlinear learning machines such as neural networks demand more computing and are more likely to have nonlinear optimization difficulties, which are more difficult to solve. Kernel methods, a recently developed technology, are strong machine learning approaches that have a less complicated architecture and give a straightforward way to transforming nonlinear optimization issues into convex optimization problems. Typical analytical tasks in kernel-based learning include classification, regression, and clustering, all of which are compromised. For image processing applications, a semisupervised deep learning approach, which is driven by a little amount of labeled data and a large amount of unlabeled data, has shown excellent performance in recent years. For their part, today’s semisupervised learning methods operate on the assumption that both labeled and unlabeled information are distributed in a similar manner, and their performance is mostly impacted by the fact that the two data sets are in a similar state of distribution as well. When there is out-of-class data in unlabeled data, the system’s performance will be adversely affected. When used in real-world applications, the capacity to verify that unlabeled data does not include data that belongs to a different category is difficult to obtain, and this is especially true in the field of synthetic aperture radar image identification (SAR). Using threshold filtering, this work addresses the problem of unlabeled input, including out-of-class data, having a detrimental influence on the performance of the model when it is utilized to train the model in a semisupervised learning environment. When the model is being trained, unlabeled data that does not belong to a category is filtered out by the model using two different sets of data that the model selects in order to optimize its performance. A series of experiments was carried out on the MSTAR data set, and the superiority of our method was shown when it was compared against a large number of current semisupervised classification algorithms of the highest level of sophistication. This was especially true when the unlabeled data had a significant proportion of data that did not fall into any of the categories. The performance of each kernel function is tested independently using two metrics, namely, the false alarm (FA) and the target miss (TM), respectively. These factors are used to calculate the proportion of incorrect judgments made using the techniques.

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Recognition and Tracking of Objects in a Clustered Remote Scene Environment

Cited by 10 publications

References 31 publications

A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence

Detecting and Mapping Individual Fruit Trees in Complex Natural Environments via UAV Remote Sensing and Optimized YOLOv5

Automated Registration of Multiangle SAR Images Using Artificial Intelligence

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