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Journal articles on the topic 'Opencv'

Author: Grafiati

Published: 4 June 2021

Last updated: 24 January 2023

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1

Domínguez, César, Jónathan Heras, and Vico Pascual. "IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine." Computers in Biology and Medicine 84 (May 2017): 189–94. http://dx.doi.org/10.1016/j.compbiomed.2017.03.027.

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2

Song, Jaehyun, Hwanjin Jeong, and Jinkyu Jeong. "Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs." Applied Sciences 12, no. 15 (August 3, 2022): 7801. http://dx.doi.org/10.3390/app12157801.

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Machine-learning-based computer vision is increasingly versatile and being leveraged by a wide range of smart devices. Due to the limited performance/energy budget of computing units in smart devices, the careful implementation of computer vision algorithms is critical. In this paper, we analyze the performance bottleneck of two well-known computer vision algorithms for object tracking: object detection and optical flow in the Open-source Computer Vision library (OpenCV). Based on our in-depth analysis of their implementation, we found the current implementation fails to utilize Open Computing Language (OpenCL) accelerators (e.g., GPUs). Based on the analysis, we propose several optimization strategies and apply them to the OpenCL implementation of object tracking algorithms. Our evaluation results demonstrate the performance of the object detection is improved by up to 86% and the performance of the optical flow by up to 10%. We believe our optimization strategies can be applied to other computer vision algorithms implemented in OpenCL.

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Guilherme, M. Pereira, Albertazzi G. Jr Armando, and E. M. Haertel Maryah. "Kamera kalibráció OpenCV használatával." Fiatal Műszakiak Tudományos Ülésszaka 1. (2014) (2014): 329–32. http://dx.doi.org/10.36243/fmtu-2014.075.

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4

Singh, Mr Devanshu. "Virtual Mouse using OpenCV." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1055–58. http://dx.doi.org/10.22214/ijraset.2021.38160.

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Abstract: This research introduces a novel method for controlling mouse movement with a real-time camera. Adding more buttons or repositioning the mouse's tracking ball are two common ways. Instead, we recommend that the hardware be redesigned. Our idea is to employ a camera and computer vision technologies to manage mouse tasks (clicking and scrolling), and we demonstrate how it can do all that existing mouse devices can. This project demonstrates how to construct a mouse control system.

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Yamamoto, Yuka. "Combining LabVIEW with OpenCV." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2018 (2018): 2P2—D03. http://dx.doi.org/10.1299/jsmermd.2018.2p2-d03.

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Sariati Syah, Riri Asyahira, and Rijal Hakiki. "The Utilization OpenCV to Measure the Water Pollutants Concentration." Journal of Environmental Engineering and Waste Management 6, no. 2 (October 4, 2021): 90. http://dx.doi.org/10.33021/jenv.v6i2.1475.

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<strong>Abstract. </strong>Intensive water quality determination needs to be adjusted with technological developments to meet today's society's needs and increased water pollution due to urbanization. Therefore, early detection is essential for in site water quality determination and as a critical consideration in making health and environmental decisions. OpenCV is a library programming feature for Computer Vision which focuses on extracting information from images in real-time, this can be considered to be potential to measure the pollutant concentration. <strong>Objectives:</strong> This study identify the potential of colorimetry analysis method by using OpenCV as an alternative method for pollutant concentration measurement<strong>. Method and results:</strong> First stage, this study collecting the data of NH3 phenate and Pt-Co CU from the spectrophotometer. The first stage also was including the development of an OpenCV code. Then, the data was collected were processed to get the concentration of NH3 and Pt-Co both using OpenCV and spectrophotometer; factors that influence the Pt-Co sample image measurement process by using OpenCV-Python was analyzed too. Then in the analysis stage, the result of the two measurement method was tested by statistic determine its significant difference. The conclusion found whether OpenCV could be potential to measure the pollutant concentration or not. <strong>Conclusion:</strong> the OpenCV has potential to be use as alternative colorimetry measurement method to determine water pollutant as there is no significant difference in the spectrophotometric method results and the results from OpenCV for Pt-Co sample. Meanwhile, in this study found that the result of NH3 from spectrophotometer is nonlinear different with from OpenCV that is linear. Thus, further research is needed to test the validity of OpenCV method. The factor influence of measurement using OpenCV code is when determining the Region of Interest (ROI) and determining the pixel values for the normalized box filter

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Krishna, Manne Vamshi, Gopu Abhishek Reddy, B. Prasanthi, and M. Sreevani. "Green Virtual Mouse Using OpenCV." International Journal of Computer Sciences and Engineering 7, no. 4 (April 30, 2019): 575–80. http://dx.doi.org/10.26438/ijcse/v7i4.575580.

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8

D., Kavitha. "Multiple Object Recognition Using OpenCV." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 5, 2021): 1736–47. http://dx.doi.org/10.47059/revistageintec.v11i2.1795.

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For automatic vision systems used in agriculture, the project presents object characteristics analysis using image processing techniques. In agriculture science, automatic object characteristics identification is important for monitoring vast areas of crops, and it detects signs of object characteristics as soon as it occurs on plant leaves. Image content characterization and supervised classifier type neural network are used in the proposed deciding method. Pre-processing, image segmentation, and detection are some of the image processing methods used in this form of decision making. An image data will be rearranged and, if necessary, a region of interest will be selected during preparation. For network training and classification, colour and texture features are extracted from an input. Colour characteristics such as mean and variance in the HSV colour space, as well as texture characteristics such as energy, contrast, homogeneity, and correlation. The device will be trained to automatically identify test images in order to assess object characteristics. With some training samples of that type, an automated classifier NN could be used for classification supported learning in this method. The tangent sigmoid function is used as the kernel function in this network. Finally, the simulated results show that the used network classifier has a low error rate during training and higher classification accuracy. In the previous researches Object detection has been made possible, but in our current research we have attempted to do live Object Detection using OpenCV and also the techniques involved in it.

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9

Lee, Sang-Young. "OpenCV-based Object Tracking System." Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology 6, no. 5 (May 31, 2016): 29–37. http://dx.doi.org/10.14257/ajmahs.2016.05.37.

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10

Pulli, Kari, Anatoly Baksheev, Kirill Kornyakov, and Victor Eruhimov. "Realtime Computer Vision with OpenCV." Queue 10, no. 4 (April 2012): 40–56. http://dx.doi.org/10.1145/2181796.2206309.

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11

Aditya Lahoty. "Traffic Light Optimization using OpenCV." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 5, 2020): 171–75. http://dx.doi.org/10.46501/ijmtst061233.

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Traffic Light Optimization aims to find the solution for an increased amount of unnecessary waiting time on traffic signals. Traffic Signal Optimization is the process of changing the timing parameters relative to the length of the green light for each traffic movement and the timed relationship between signalized intersections using a computer software program. Our project aims to set the timer of green light based on real-time traffic congestion i.e. number of vehicles in a particular direction of the traffic light. To work in this project, we are using the OpenCV method to detect vehicles and then perform our calculation in the algorithm to predict the time for the green light to be in an active state.

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12

Zulkhaidi, Tengku Cut Al-Saidina, Eny Maria, and Yulianto Yulianto. "Pengenalan Pola Bentuk Wajah dengan OpenCV." Jurnal Rekayasa Teknologi Informasi (JURTI) 3, no. 2 (June 30, 2020): 181. http://dx.doi.org/10.30872/jurti.v3i2.4033.

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Pada penelitian ini akan menggunakan module OpenCV pada bahasa pemrograman python untuk mengenali wajah sesorang yang menggunakan Haar Cascades untuk mengenali bentuk wajah dan mata. Tahapan awal menggunakan open source dari intel untuk data wajah dan mata, dipadukan dengan module cascade classifier pada openCV untuk merubah data menjadi pengenalan bentuk wajah dari titik pada wajah yang dianggap sesuai dengan data yang telah disediakan. Banyak dari beberapa sistem pendeteksian wajah menggunakan metode computer vision sebagai metode pendeteksi objek. Metode computer vision dikenal memiliki kecepatan dan keakuratan yang tinggi karena menggabungkan beberapa konsep (Haar Features, Integral Image, AdaBoost, dan Cascade Classifier) menjadi sebuah metode utama untuk mendeteksi objek. Banyak dari sistem deteksi tersebut menggunakan C atau C++ sebagai bahasa pemrograman, dan OpenCV sebagai librari deteksi objek. Hal ini dikarenakan librari OpenCV menerapkan metode computer vision kedalam sistem deteksinya, sehingga memudahkan dalam pembuatan sistem. Penelitian ini bertujuan untuk mengimplementasikan computer vision ke dalam sistem deteksi wajah sederhana dengan memanfaatkan library yang ada pada OpenCV dan memanfaatkan bahasa pemrograman Python sebagai pondasi sistem.

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Lia Farokhah. "Perbandingan Metode Deteksi Wajah Menggunakan OpenCV Haar Cascade, OpenCV Single Shot Multibox Detector (SSD) dan DLib CNN." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 3 (June 29, 2021): 609–14. http://dx.doi.org/10.29207/resti.v5i3.3125.

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Comparison of methods in face detection is needed to provide recommendation of best method. This study compared three methods in face detection, namely OpenCV haar cascade, OpenCV Single Shot Multibox Detector (SSD) and Dlib CNN. Face detection is focused on five challenging conditions, namely face detection in head position obstacles, wearing face masks, lighting, background images that have a lot of noise, differences in expression. Data testing is taken randomly on google with reference to one image consisting of more than one detected face with wild condition. The results of the comparative analysis in wild condition show that the OpenCV haar cascade has more weaknesses with a performance percentage of 20% compared OpenCV SSD and Dlib CNN method. Performance results of SSD and Dlib CNN have the same performance in the five conditions tested, which is about 80%.

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Sejati, RR Puji Hajar, and Rodhiyah Mardhiyyah. "Deteksi Wajah Berbasis Facial Landmark Menggunakan OpenCV Dan Dlib." Jurnal Teknologi Informasi 5, no. 2 (December 31, 2021): 144–48. http://dx.doi.org/10.36294/jurti.v5i2.2220.

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Computer science and information technology have advanced in a variety of sectors that were previously unachievable due to constraints such as hardware. Computer vision can be used to recognize an object using computer science. Objects can be recognized by taking or recording photos or videos and then processing them using specific tools and methodologies. The goal of the facial landmark-based face detection research using OpenCV and Dlib is to perform face detection in people so that it can be used for a variety of purposes in the future. The strategy employed in this study was the usage of facial landmarks using OpenCV and Dlib to improve face detection accuracy. Face detection has been effectively carried out based on facial landmark points, according to the findings of testing the entire system. Face landmark-based face detection is more accurate using the OpenCV Dlib, which can be seen during processing in the OpenCV Dlib, which can precision photos based on facial movements.

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15

Balázs, Viktor, László Szilágyi, Antal Apagyi, and Timotei István Erdei. "OpenCV alapú táblafelismerő videóelemző szoftver létrehozása." Műszaki Tudományos Közlemények 9, no. 1 (2018): 39–42. http://dx.doi.org/10.33895/mtk-2018.09.05.

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16

Tóth, Kálmán. "Az Opencv lehetőségei a mérnöki munkában." Fiatal Műszakiak Tudományos Ülésszaka 1. (2010) (2010): 333–36. http://dx.doi.org/10.36243/fmtu-2010.79.

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17

Cai, Jianjun, Erxin Sun, and Zongjuan Chen. "OCR Service Platform Based on OpenCV." Journal of Physics: Conference Series 1883, no. 1 (April 1, 2021): 012043. http://dx.doi.org/10.1088/1742-6596/1883/1/012043.

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18

Jain, Pratiksha, Neha Chopra, and Vaishali Gupta. "Automatic License Plate Recognition using OpenCV." International Journal of Computer Applications Technology and Research 3, no. 12 (December 1, 2014): 756–61. http://dx.doi.org/10.7753/ijcatr0312.1001.

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19

Kumar, Ajay, Shivansh Chaudhary, Sonik Sangal, and Raj Dhama. "Face Detection and Recognition using OpenCV." International Journal of Computer Applications 184, no. 11 (May 20, 2022): 23–32. http://dx.doi.org/10.5120/ijca2022922085.

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20

Sravan Kumar, G., Gardas Mythri, G. Tejaswini, Kakarla Ayesha Bhanu, and Kusa Vaishnavi. "EYE BALL CURSOR MOVEMENT USING OPENCV." YMER Digital 21, no. 05 (May 12, 2022): 534–39. http://dx.doi.org/10.37896/ymer21.05/60.

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In this study, a specific human computer interaction system using eyeball movement is presented. Conventionally, computer system uses mouse as one of the data input devices. But in this system, we use eyes instead of mouse which provides a unique way of operating the computer with the help of eyeball movements. The implementation work underlying this system for pupil identification uses OPENCV library to control the cursor of the personal computer and moreover Eye Aspect Ratio technique is ascertained along with Dlib to detect the pupil. This system tracks the eye movements of the user with an IP cam (Internet Protocol camera) and simulates the eye movements into mouse cursor movements on screen and also detects user’s eye staring on icon and will translate it into click operation on screen. The main aim of this system is to help the user to control the cursor without the use of hands and is of great use especially for the people with disability.

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21

B, Chandra, Kanaga Suba Raja S, Rohit M, and R. Sriram Vignesh. "Eyeball Movement Cursor Control Using OpenCV." ECS Transactions 107, no. 1 (April 24, 2022): 10005–11. http://dx.doi.org/10.1149/10701.10005ecst.

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The upcoming generation is going to be evolved with an interaction that will be nonverbal, which is called the eye gaze and it also develops a new mode of communication for disabled people. The basic need for this kind of a system is that it can provide the assistance that a third person gives for the physically disabled people and hence by the means of tracking the eyeball movement this is made possible. The right, left, top, and bottom movements are incorporated concerning the movement of the eyeball. The advanced version of this system comes with a chai that has wheels on the ends, hence this can also be mounted with the chair and hence it makes the user take the system wherever and everywhere he moves around. The tracking is generally done by taking the trace of the center of the pupil and hence with the traces it generates the center, top, right, left, and bottom.

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22

K, Kiran Kumar, and Dr Mohammed Tajammul. "Monitoring Social Distancing Detection using OpenCV." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 2801–3. http://dx.doi.org/10.22214/ijraset.2022.41722.

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23

Uranishi, Yuki. "OpenCV: Open Source Computer Vision Library." Journal of The Institute of Image Information and Television Engineers 72, no. 9 (2018): 736–39. http://dx.doi.org/10.3169/itej.72.736.

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Qiao, Yu Jing, Zhong He Liu, Da Xun Hu, and Jing Wei Xu. "Camera Calibration Method Based on OpenCV." Applied Mechanics and Materials 330 (June 2013): 517–20. http://dx.doi.org/10.4028/www.scientific.net/amm.330.517.

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A new calibration method is proposed on the basic of OpenCV camera model and existing calibration method. The method can be accomplished in three concerted steps. Firstly, standardization homography matrix is obtained through camera linear model namely normalized the elements in lower right corner of the original matrix, then the intrinsic parameters matrix and external parameter initial value of camera can be calibrated through solving the statically indeterminate equations with the least square method. Secondly, concerning lens distortion, nonlinear model is obtained, and using intersection point coordinate of chess board and initial value of the first step, initial value of distortion parameters is given through the least square method. Finally, all the camera parameters will nonlinear optimized.

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Lee, MyounJae. "Production of Media Art using OpenCV." Journal of the Korea Convergence Society 7, no. 4 (August 31, 2016): 173–80. http://dx.doi.org/10.15207/jkcs.2016.7.4.173.

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S.V, Viraktamath, Mukund Katti, Aditya Khatawkar, and Pavan Kulkarni. "Face Detection and Tracking using OpenCV." SIJ Transactions on Computer Networks & Communication Engineering 04, no. 03 (June 2, 2016): 01–06. http://dx.doi.org/10.9756/sijcnce/v4i3/0103540102.

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27

Triyono, L., E. H. Pratisto, S. A. T. Bawono, F. A. Purnomo, Y. Yudhanto, and B. Raharjo. "Sign Language Translator Application Using OpenCV." IOP Conference Series: Materials Science and Engineering 333 (March 2018): 012109. http://dx.doi.org/10.1088/1757-899x/333/1/012109.

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28

Pulli, Kari, Anatoly Baksheev, Kirill Kornyakov, and Victor Eruhimov. "Real-time computer vision with OpenCV." Communications of the ACM 55, no. 6 (June 2012): 61–69. http://dx.doi.org/10.1145/2184319.2184337.

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29

Xie, Guobo, and Wen Lu. "Image Edge Detection Based On Opencv." International Journal of Electronics and Electrical Engineering 1, no. 2 (2013): 104–6. http://dx.doi.org/10.12720/ijeee.1.2.104-106.

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Hussein, Arezu Rezgar, and Rasber Dhahir Rashid. "KurdFace Morph Dataset Creation Using OpenCV." Science Journal of University of Zakho 10, no. 4 (December 14, 2022): 258–67. http://dx.doi.org/10.25271/sjuoz.2022.10.4.943.

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Automated facial recognition is rapidly being used to reliably identify the identities of individuals for a variety of applications, from automated border control to unlocking mobile phones. The attack of Morphing has presented a significant risk to the face recognition system (FRS) at automated border control. Face morphing is a technique for blending the facial images of two or more people such that the outcome looks like both of them. For example, a morphing attack may be used to get a fake passport by using a morphed image. This passport can be used by both the modified image contributors while crossing the border. Due to the publicly available digital altering tools that criminals may use to carry out face morphing attacks. Morph Attack Detection (MAD) systems have received a lot of attention in recent years. In the absence of automated morphing detection, Face Recognition Systems (FRS) are extremely susceptible to morphing attacks. Due to the limited number of publicly available face morph datasets to investigate, especially to our knowledge, there is no Kurdish morph dataset. In this work, we decided to generate a new face dataset, including morphed images which we named as "KurdFace" dataset. OpenCV was used to generate morphed images. Then we study the susceptibility of biometric systems to such morphed face attacks by designing and creating a Morph Attack Detection model to distinguish morphed images from genuine ones. To evaluate the robustness of our dataset regarding morphing attack detection, we compare it with the AMSL dataset to determine the classification error rate on both datasets to see how our dataset is different from others. Local Binary Pattern and Uniform Local Binary Pattern are used as feature extraction techniques, and as a classifier, SVM is utilized. The experimental result shows that our dataset is suitable for research purposes.

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Zelinsky, Alex. "Learning OpenCV---Computer Vision with the OpenCV Library (Bradski, G.R. et al.; 2008)[On the Shelf]." IEEE Robotics & Automation Magazine 16, no. 3 (September 2009): 100. http://dx.doi.org/10.1109/mra.2009.933612.

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32

Wu, Wen Huan, Ying Jun Zhao, and Yong Fei Che. "Research and Implementation of Face Detection Based on OpenCV." Advanced Materials Research 971-973 (June 2014): 1710–13. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1710.

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Face detection is the key point in automatic face recognition system. This paper introduces the face detection algorithm with a cascade of Adaboost classifiers and how to configure OpenCV in MCVS. Using OpenCV realized the face detection. And a detailed analysis of the face detection results is presented. Through experiment, we found that the method used in this article has a high accuracy rate and better real-time.

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Ismail, Ahmad Puad, Farah Athirah Abd Aziz, Nazirah Mohamat Kasim, and Kamarulazhar Daud. "Hand gesture recognition on python and opencv." IOP Conference Series: Materials Science and Engineering 1045, no. 1 (February 1, 2021): 012043. http://dx.doi.org/10.1088/1757-899x/1045/1/012043.

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Pomaska, G. "STEREO VISION APPLYING OPENCV AND RASPBERRY PI." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W17 (November 29, 2019): 265–69. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w17-265-2019.

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Abstract. This article points out the single board computer Raspberry Pi and the related camera modules for image acquisition. Particular attention is directed to stereoscopic image recording and post processing software applying OpenCV. A design of a camera network is created and applied to a field application. The OpenCV computer vision library and its Python binding provides some script samples to encourage users developing their own custom tailored scripts. Stereoscopic recording is intended for extended base lines without a mechanical bar. Image series will be taken in order to wipe out moving objects from the frames. And finally the NoIR camera made infrared photography possible with low effort. Computer, accupack and lens board are assembled in a 3D printed housing operated by a mobile device.

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p, Pandiaraja. "Attendance Automation by Facial Recognition Using OpenCV." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 2 (April 25, 2020): 925–29. http://dx.doi.org/10.30534/ijatcse/2020/03922020.

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Jafri, Afshan. "Efficient Dynamic Multiple GPGPU Layer for OpenCV." International Journal of Computer Applications 164, no. 3 (April 17, 2017): 42–48. http://dx.doi.org/10.5120/ijca2017913604.

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N, Deeksha, Namitha M. R. Maiya, Varsha S, and Deepashree R. "LICENSE PLATE DETECTION METHODS BASED ON OPENCV." International Research Journal of Computer Science 9, no. 8 (August 13, 2022): 316–20. http://dx.doi.org/10.26562/irjcs.2022.v0908.31.

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With the popularization of automobile and the progress of computer vision detection technology, intelligent license plate detection technology has gradually become an important part of intelligent traffic management. License plate detection is used to segment vehicle image and obtain license plate area for follow-up recognition system to screen. It is widely used in intelligent traffic management, vehicle video monitoring and other fields. In this paper, two license plate detection methods are studied, one is based on Sobel edge detection and the other is based on morphological gradient detection. Basing on OpenCV and visual studio 2012 under Windows system, two methods of license plate detection are implemented, and the two algorithms are compared in detail from the aspects of license plate detection accuracy. These methods have high efficiency and good interactivity, which provide a reference for later license platere cognition.

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Kulkarni, Mr Anand, Mr Shubham Salunkhe, and Prof Mrs Shantiguru. "E - Attendance System Using Opencv and CNN." IJARCCE 8, no. 4 (April 30, 2019): 335–40. http://dx.doi.org/10.17148/ijarcce.2019.8456.

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Weibin Wu, Renjie Hu, Tiansheng Hong, Lei Zhi, Ben Zhao, Shaomeng Ruan, and Yu Li. "Adaptive Traffic Light System Based On OpenCV." International Journal of Advancements in Computing Technology 5, no. 7 (April 15, 2013): 419–26. http://dx.doi.org/10.4156/ijact.vol5.issue7.51.

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Shafi, Gowher. "Object Tracking Using HSV Values and OpenCV." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 594–99. http://dx.doi.org/10.22214/ijraset.2021.39238.

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Abstract: This research shows how to use colour and movement to automate the process of recognising and tracking things. Video tracking is a technique for detecting a moving object over a long distance using a camera. The main purpose of video tracking is to connect target objects in subsequent video frames. The connection may be particularly troublesome when things move faster than the frame rate. Using HSV colour space values and OpenCV in different video frames, this study proposes a way to track moving objects in real-time. We begin by calculating the HSV value of an item to be monitored, and then we track the object throughout the testing step. The items were shown to be tracked with 90 percent accuracy. Keywords: HSV, OpenCV, Object tracking, Video frames, GUI

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Parikh, Dhruv Piyush. "Autonomous Gateway Architecture for Security Using OpenCV." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1488–93. http://dx.doi.org/10.22214/ijraset.2021.39032.

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Abstract: Today as we can see security for anything is considered to be a very important part of our livelihood and we need to seek more and more security every day in this fast growing world. As the security of public parking lots increases day by day and to ensure safety, many people are required in this job that increases the cost of security So we have looked into the process and came up with a plan to use computer vision for the security purpose which will reduce the manpower required for work instead with machine intelligence. We are going to use Computer Vision to mask the license plate and save it with the entry and exit time. This research paper will enhance the security provided by a CCTV camera in any public parking and will also keep the record of every car entering and exiting the parking area. Keywords: OpenCV, Machine Learning, EasyOCR, SQLite, Image Contour Processing

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Gong, Suning, Zhiying Huan, Mingmei JI, Xinxin Chen, and Yuanqiu Bao. "ITLCS Based on OpenCV Image Processing Technology." Journal of Physics: Conference Series 2143, no. 1 (December 1, 2021): 012031. http://dx.doi.org/10.1088/1742-6596/2143/1/012031.

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Abstract With the rapid growth of the number of motor vehicles in the city, traffic congestion is more serious every day, part of it is caused by the coding delay with the red light on, not real traffic jams, now we need a control system that can really change the traffic flow. In this paper, ITLCS (intelligent traffic signal control system) based on OpenCV image processing technology is proposed to adjust the timing of traffic signal according to road density, instead of setting a level that is balanced with other lanes, so that high-load traffic lanes can be used for a long time. The camera facing the roadway in the system takes pictures of the driving route, then takes pictures of the driving density of pedestrians and vehicles, and compares each image through processing technology, after the system is processed, the traffic light signal timing can be adjusted immediately, which greatly reduces the time spent on the inactive green light and can effectively deal with the traffic congestion problem.

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Ghoshal, Abhishek, Aditya Aspat, and Elton Lemos. "OpenCV Image Processing for AI Pet Robot." International Journal of Applied Sciences and Smart Technologies 03, no. 01 (June 21, 2021): 65–82. http://dx.doi.org/10.24071/ijasst.v3i1.2765.

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The Artificial Intelligence (AI) Pet Robot is a culmination of multiple fields of computer science. This paper showcases the capabilities of our robot. Most of the functionalities stem from image processing made available through OpenCV. The functions of the robot discussed in this paper are face tracking, emotion recognition and a colour-based follow routine. Face tracking allows the robot to keep the face of the user constantly in the frame to allow capturing of facial data. Using this data, emotion recognition achieved an accuracy of 66% on the FER-2013 dataset. The colour-based follow routine enables the robot to follow the user as they walk based on the presence of a specific colour.

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G V, Balaji. "Object Detection using OpenCV and Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 3920–23. http://dx.doi.org/10.22214/ijraset.2021.35880.

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Object Detection using SSD (Single Shot Detector) and MobileNets are efficient because this technique detects objects quickly with less resourses without sacrificing performance. In this every class of item for which the classification algorithm has been trained generates a bounding box and an annotation describing that class of object. This provides the foundation for creating several types of analytical features such as the volume of traffic in a certain area over time or the entire population in an area is real-time detection and categorization of objects from video data.

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Kanade, Dnyaneshwar, Aseem Patil, Venugopal Bang, Maithili Jayale, Darshan Dodal, and Rutik Katkamvar. "DRIVER ALERTNESS DETECTION USING OPENCV IN PYTHON." International Journal of Engineering Applied Sciences and Technology 04, no. 06 (December 1, 2019): 99–102. http://dx.doi.org/10.33564/ijeast.2019.v04i06.015.

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Kagami, Shingo. "Utilizing OpenCV for High-Speed Vision Processing." Journal of the Robotics Society of Japan 31, no. 3 (2013): 244–48. http://dx.doi.org/10.7210/jrsj.31.244.

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Agus Suryawibawa, I. Wayan, I. Ketut Gede Darma Putra, and Ni Kadek Ayu Wirdiani. "Herbs Recognition Based on Android using OpenCV." International Journal of Image, Graphics and Signal Processing 7, no. 2 (January 8, 2015): 1–7. http://dx.doi.org/10.5815/ijigsp.2015.02.01.

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Sharma, Mr Siddharth. "Human Activity Recognition using OpenCv & Python." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (May 31, 2020): 677–81. http://dx.doi.org/10.22214/ijraset.2020.5106.

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Lu, Yuncong. "Handwritten capital letter recognition based on OpenCV." MATEC Web of Conferences 277 (2019): 02030. http://dx.doi.org/10.1051/matecconf/201927702030.

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Handwriting capitalization recognition is a function of distinguishing handwritten capital letters by means of machine or computer intelligence, which is classified into the field of optical character recognition. Given that capital letters are widely used around the world, identification and analysis are often used as the main components of some control systems. Therefore, the research on handwritten capital letter recognition is also very practical and has important practical significance. The key part of the research contained in this paper is the image preprocessing and the optimal selection of feature vectors, and finally completes the design of handwritten digit recognition system. In this paper, the Fourier and Bayesian commonly used are compared, and eventually the Fourier transform feature is applied to the system classification identification. After completing the test on the relevant experimental data, the results show that the handwritten capital recognition system established in this paper has a high recognition accuracy for handwritten capital letters after repeated training.

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Janku, Peter, Karel Koplik, Tomas Dulik, and Istvan Szabo. "Comparison of tracking algorithms implemented in OpenCV." MATEC Web of Conferences 76 (2016): 04031. http://dx.doi.org/10.1051/matecconf/20167604031.

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