OpenCV Basics: A Mobile Application to Support the Teaching of Computer Vision Concepts

Sigut, J.; Castro, Miguel Ramón González; Arnay, Rafael; Sigut, M.

doi:10.1109/te.2020.2993013

Cited by 44 publications

(35 citation statements)

References 10 publications

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“…Today, there is a need to attract more students to study computer vision. Curricula of neural networks study exist in some universities in computer science specialties [31,32]. Availability of resources (such as OpenCV) allows students to engage in tasks such as image analysis and processing, facial recognition systems, object identification, motion tracking, motion analysis, posture assessment, object identification, image segmentation, video tracking and other.…”

Section: Resultsmentioning

confidence: 99%

Review of state of computer vision technologies development in the world and Ukraine

Valko

Osadchyi

2022

J. Phys.: Conf. Ser.

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Computer Vision is a multidisciplinary field, a unit of artificial intelligence and machine learning. It includes the specialized methods and general learning algorithms use. More and more scientists are becoming interested in Computer Vision researching. Therefore, it is important to train future professionals in this area and develop special study programs. We reviewed the development of Computer Vision technologies in the world and in Ukraine, considered the challenges facing professionals and gave an example of Computer Vision project development.

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Section: Resultsmentioning

confidence: 99%

Review of state of computer vision technologies development in the world and Ukraine

Valko

Osadchyi

2022

J. Phys.: Conf. Ser.

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“…Pedestrian detection is one of the important parts of automated vehicle sensing systems and has been researched for many years. It is mainly achieved through the neural network [3][4][5] and open-source computer vision library (OpenCV) [6]. Among them, the convolutional neural network (CNN) proposed by Lecun [7] was representative.…”

Section: Related Workmentioning

confidence: 99%

Advanced Pedestrian State Sensing Method for Automated Patrol Vehicle Based on Multi-Sensor Fusion

Wang

Cheng

Wang

et al. 2022

Sensors

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At present, the COVID-19 pandemic still presents with outbreaks occasionally, and pedestrians in public areas are at risk of being infected by the viruses. In order to reduce the risk of cross-infection, an advanced pedestrian state sensing method for automated patrol vehicles based on multi-sensor fusion is proposed to sense pedestrian state. Firstly, the pedestrian data output by the Euclidean clustering algorithm and the YOLO V4 network are obtained, and a decision-level fusion method is adopted to improve the accuracy of pedestrian detection. Then, combined with the pedestrian detection results, we calculate the crowd density distribution based on multi-layer fusion and estimate the crowd density in the scenario according to the density distribution. In addition, once the crowd aggregates, the body temperature of the aggregated crowd is detected by a thermal infrared camera. Finally, based on the proposed method, an experiment with an automated patrol vehicle is designed to verify the accuracy and feasibility. The experimental results have shown that the mean accuracy of pedestrian detection is increased by 17.1% compared with using a single sensor. The area of crowd aggregation is divided, and the mean error of the crowd density estimation is 3.74%. The maximum error between the body temperature detection results and thermometer measurement results is less than 0.8°, and the abnormal temperature targets can be determined in the scenario, which can provide an efficient advanced pedestrian state sensing technique for the prevention and control area of an epidemic.

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“…Developing a quick and powerful algorithm using OpenCV will guarantee real time identification of image attributes. This is the source code of OpenCV that is being executed in the Python framework that could be used for live real time identification from the web camera view [6].…”

Section: Image Analytics Using Open Computer Visionmentioning

confidence: 99%

Dynamic Image Forensics and Forgery Analytics using Open Computer Vision Framework

Sumathi¹

2021

WJCMS

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The key advances in Computer Vision and Optical Image Processing are the emerging technologies nowadays in diverse fields including Facial Recognition, Biometric Verifications, Internet of Things (IoT), Criminal Investigation, Signature Identification in banking and several others. Thus, these applications use image and live video processing for facilitating different applications for analyzing and forecasting." Computer vision is used in tons of activities such as monitoring, face recognition, motion recognition, object detection, among many others. The development of social networking platforms such as Facebook and Instagram led to an increase in the volume of image data that was being generated. Use of image and video processing software is a major concern for Facebook because the photos and videos that people post to the social network are doctored images. These kind of images are frequently cited as fake and used in malevolent ways such as motivating violence and death. You need to authenticate the questionable images before take action. It is very hard to ensure photo authenticity due to the power of photo manipulations. Image formation can be determined by image forensic techniques. The technique of image duplication is used to conceal missing areas.

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OpenCV Basics: A Mobile Application to Support the Teaching of Computer Vision Concepts

Cited by 44 publications

References 10 publications

Review of state of computer vision technologies development in the world and Ukraine

Review of state of computer vision technologies development in the world and Ukraine

Advanced Pedestrian State Sensing Method for Automated Patrol Vehicle Based on Multi-Sensor Fusion

Dynamic Image Forensics and Forgery Analytics using Open Computer Vision Framework

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