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Journal articles on the topic 'Camera recognition'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

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1

Zhao, Ruiyi, Yangshi Ge, Ye Duan, and Quanhong Jiang. "Large-field Gesture Tracking and Recognition for Augmented Reality Interaction." Journal of Physics: Conference Series 2560, no. 1 (2023): 012016. http://dx.doi.org/10.1088/1742-6596/2560/1/012016.

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Abstract In recent years, with the continuous development of computer vision and artificial intelligence technology, gesture recognition is widely used in many fields, such as virtual reality, augmented reality and so on. However, the traditional binocular camera architecture is limited by its limited field of view Angle and depth perception range. Fisheye camera is gradually applied in gesture recognition field because of its advantage of larger field of view Angle. Fisheye cameras offer a wider field of vision than previous binocular cameras, allowing for a greater range of gesture recogniti

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WANG, Chenyu, Yukinori KOBAYASHI, Takanori EMARU, and Ankit RAVANKAR. "1A1-H04 Recognition of 3-D Grid Structure Recognition with Fixed Camera and RGB-D Camera." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2015 (2015): _1A1—H04_1—_1A1—H04_4. http://dx.doi.org/10.1299/jsmermd.2015._1a1-h04_1.

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Reddy, K. Manideep. "Face Recognition for Criminal Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2856–60. http://dx.doi.org/10.22214/ijraset.2022.44528.

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Abstract: In these days, assessment camera structure wins as a security system at high speed since this structure can screen from remote spots using Web camera joined to video screen by network. Besides, computerized supplies like Web camera, and hard circle drive are proficiently fabricated, and are sold for minimal price. Likewise, execution gain of these mechanized sorts of stuff improves at a fast rate. Current perception camera structure shows dynamic pictures from some oversight areas shot by various Web cameras all the while. Then, this system makes spectator's mind and body tired consi

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Guo, Yunsong. "Autonomous Vehicle Navigation Based on Vision and Mapless Strategies." Highlights in Science, Engineering and Technology 106 (July 16, 2024): 68–72. http://dx.doi.org/10.54097/g4r19w17.

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Vision camera recognition system has many roles as face recognition can be used in the criminal system, object recognition can quickly classify items, and behavioral recognition can be placed in the intelligent cockpit to identify and analyze the driver's behavior and make the correct judgment and decision-making. Vision cameras can also be used in automatic automobile driving because the camera will not be distracted and can be installed with multiple cameras to observe simultaneously, so the camera can constantly observe the surrounding situation, which is incomparable to human vision. Maple

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Yuan, Mingxin, Jie Li, Borui Cao, Shihao Bao, Li Sun, and Xiangbin Li. "Research on Target Hybrid Recognition and Localization Methods Based on an Industrial Camera and a Depth Camera in Complex Scenes." Electronics 13, no. 22 (2024): 4381. http://dx.doi.org/10.3390/electronics13224381.

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In order to improve the target visual recognition and localization accuracy of robotic arms in complex scenes with similar targets, hybrid recognition and localization methods based on an industrial camera and depth camera are proposed. First, according to the speed and accuracy requirements of target recognition and localization, YOLOv5s is introduced as the basic algorithm model for target hybrid recognition and localization. Then, in order to improve the accuracy of target recognition and coarse localization based on an industrial camera (eye-to-hand), the AFPN feature fusion module, simple

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Chen, Zhuo, Hai Bo Wu, and Sheng Ping Xia. "A Cooperative Dual-Camera System for Face Recognition and Video Monitoring." Advanced Materials Research 998-999 (July 2014): 784–88. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.784.

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In the ordinary video monitoring system, the whole small scene is usually observed by a stationary camera or a few stationary cameras, but the system can’t zoom and focus on the target of interest rapidly, and also can’t get the high resolution image of the target of interest in a far distance. Therefore based on the research of the dual-camera cooperation and a RSOM clustering tree and CSHG algorithm, a cooperative dual-camera system is designed to track and recognize a face quickly in a large-scale and far-distance scene in this paper, which is made up of a Stationary Wide Field of View (SWF

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Tseng, Hung Li, Chao Nan Hung, Sun Yen Tan, Chiu Ching Tuan, Chi Ping Lee, and Wen Tzeng Huang. "Single Camera for Multiple Vehicles License Plate Localization and Recognition on Multilane Highway." Applied Mechanics and Materials 418 (September 2013): 120–23. http://dx.doi.org/10.4028/www.scientific.net/amm.418.120.

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License plate recognition systems can be classified into several categories: systems with single camera for motionless vehicle, systems with single camera for moving vehicle, and systems with multiple cameras for moving vehicles on highways (one camera for each lane). In this paper we present an innovative system which can locate multiple moving vehicles and recognize their license plates with only one single camera. Obviously, our system is highly cost effective in comparison with other systems. Our system has license plate localization success rate 94% and license plate recognition success r

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Francisca O Nwokoma, Juliet N Odii, Ikechukwu I Ayogu, and James C Ogbonna. "Camera-based OCR scene text detection issues: A review." World Journal of Advanced Research and Reviews 12, no. 3 (2021): 484–89. http://dx.doi.org/10.30574/wjarr.2021.12.3.0705.

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Camera-based scene text detection and recognition is a research area that has attracted countless attention and had made noticeable progress in the area of deep learning technology, computer vision, and pattern recognition. They are highly recommended for capturing text on-scene images (signboards), documents with a multipart and complex background, images on thick books and documents that are highly fragile. This technology encourages real-time processing since handheld cameras are built with very high processing speed and internal memory, are quite easy and flexible to use than the tradition

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Francisca, O. Nwokoma, N. Odii Juliet, I. Ayogu Ikechukwu, and C. Ogbonna James. "Camera-based OCR scene text detection issues: A review." World Journal of Advanced Research and Reviews 12, no. 3 (2021): 484–89. https://doi.org/10.5281/zenodo.5813901.

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Camera-based scene text detection and recognition is a research area that has attracted countless attention and had made noticeable progress in the area of deep learning technology, computer vision, and pattern recognition. They are highly recommended for capturing text on-scene images (signboards), documents with a multipart and complex background, images on thick books and documents that are highly fragile. This technology encourages real-time processing since handheld cameras are built with very high processing speed and internal memory, are quite easy and flexible to use than the tradition

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Fan, Zhijie, Zhiwei Cao, Xin Li, Chunmei Wang, Bo Jin, and Qianjin Tang. "Video Surveillance Camera Identity Recognition Method Fused With Multi-Dimensional Static and Dynamic Identification Features." International Journal of Information Security and Privacy 17, no. 1 (2023): 1–18. http://dx.doi.org/10.4018/ijisp.319304.

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With the development of smart cities, video surveillance networks have become an important infrastructure for urban governance. However, by replacing or tampering with surveillance cameras, an important front-end device, attackers are able to access the internal network. In order to identify illegal or suspicious camera identities in advance, a camera identity identification method that incorporates multidimensional identification features is proposed. By extracting the static information of cameras and dynamic traffic information, a camera identity system that incorporates explicit, implicit,

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Ake, Kanako, Tadatoshi Ogura, Yayoi Kaneko, and Gregory S. A. Rasmussen. "Automated photogrammetric method to identify individual painted dogs (Lycaon pictus)." Zoology and Ecology 29, no. 2 (2019): 103–8. http://dx.doi.org/10.35513/21658005.2019.2.5.

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The painted dog, Lycaon pictus, has been visually identified by their tricolor patterns in surveys and whilst computerised recognition methods have been used in other species, they have not been used in painted dogs. This study compares results achieved from Hotspotter software against human recognition. Fifteen individual painted dogs in Yokohama Zoo, Japan were photographed using camera-traps and hand-held cameras from October 17–20, 2017. Twenty examinees identified 297 photos visually, and the same images were identified using Hotspotter. In the visual identification, mean accuracy rate wa

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Rusydi, Muhammad Ilhamdi, Aulia Novira, Takayuki Nakagome, et al. "Autonomous Movement Control of Coaxial Mobile Robot based on Aspect Ratio of Human Face for Public Relation Activity Using Stereo Thermal Camera." Journal of Robotics and Control (JRC) 3, no. 3 (2022): 361–73. http://dx.doi.org/10.18196/jrc.v3i3.14750.

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In recent years, robots that recognize people around them and provide guidance, information, and monitoring have been attracting attention. The mainstream of conventional human recognition technology is the method using a camera or laser range finder. However, it is difficult to recognize with a camera due to fluctuations in lighting 1), and it is often affected by the recognition environment such as misrecognition 2) with a person's leg and a chair's leg with a laser range finder. Therefore, we propose a human recognition method using a thermal camera that can visualize human heat. This study

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Park, Yeonji, Yoojin Jeong, and Chaebong Sohn. "Suspicious behavior recognition using deep learning." Journal of Advances in Military Studies 4, no. 1 (2021): 43–59. http://dx.doi.org/10.37944/jams.v4i1.78.

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The purpose of this study is to reinforce the defense and security system by recognizing the behaviors of suspicious person both inside and outside the military using deep learning. Surveillance cameras help detect criminals and people who are acting unusual. However, it is inefficient in that the administrator must monitor all the images transmitted from the camera. It incurs a large cost and is vulnerable to human error. Therefore, in this study, we propose a method to find a person who should be watched carefully only with surveillance camera images. For this purpose, the video data of doub

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Zamorano, Chandra I., Kiki Prawiroredjo, E. Shintadewi Julian, and Endang Djuana. "Rancang Bangun Sistem Kamera Pengawas dengan Pengenalan Wajah untuk Keamanan Berbasis Blynk Legacy." Techné : Jurnal Ilmiah Elektroteknika 22, no. 2 (2023): 241–58. http://dx.doi.org/10.31358/techne.v22i2.381.

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Covid-19 pandemic that has occurred since the beginning of 2020 has brought down all aspects of the country, starting from community activities to the economy. This has an impact on increasing the number of crimes committed by the community such as theft, robbery or other crimes. In this study, a room security system is proposed that uses a surveillance camera with a face recognition ability that records the face image of an intruder and records events as evidence of an intrusion. This system sends information quickly and automatically to the Android application user if an intruder who the cam

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Holešovský, Ondřej, Radoslav Škoviera, Václav Hlaváč, and Roman Vítek. "Experimental Comparison between Event and Global Shutter Cameras." Sensors 21, no. 4 (2021): 1137. http://dx.doi.org/10.3390/s21041137.

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We compare event-cameras with fast (global shutter) frame-cameras experimentally, asking: “What is the application domain, in which an event-camera surpasses a fast frame-camera?” Surprisingly, finding the answer has been difficult. Our methodology was to test event- and frame-cameras on generic computer vision tasks where event-camera advantages should manifest. We used two methods: (1) a controlled, cheap, and easily reproducible experiment (observing a marker on a rotating disk at varying speeds); (2) selecting one challenging practical ballistic experiment (observing a flying bullet having

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Astrid, Marcella, and Seung‐Ik Lee. "Assembling three one‐camera images for three‐camera intersection classification." ETRI Journal 45, no. 5 (2023): 862–73. http://dx.doi.org/10.4218/etrij.2023-0100.

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AbstractDetermining whether an autonomous self‐driving agent is in the middle of an intersection can be extremely difficult when relying on visual input taken from a single camera. In such a problem setting, a wider range of views is essential, which drives us to use three cameras positioned in the front, left, and right of an agent for better intersection recognition. However, collecting adequate training data with three cameras poses several practical difficulties; hence, we propose using data collected from one camera to train a three‐camera model, which would enable us to more easily compi

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WANG, Lei, Chao HU, Jie WU, Qing HE, and Wei LIU. "Multi-camera face gesture recognition." Journal of Computer Applications 30, no. 12 (2011): 3307–10. http://dx.doi.org/10.3724/sp.j.1087.2010.03307.

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Bernardi, Bryan D. "Camera on-board voice recognition." Journal of the Acoustical Society of America 101, no. 5 (1997): 2429. http://dx.doi.org/10.1121/1.418474.

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Athanasiadou, Eleni, Zeno Geradts, and Erwin Van Eijk. "Camera recognition with deep learning." Forensic Sciences Research 3, no. 3 (2018): 210–18. http://dx.doi.org/10.1080/20961790.2018.1485198.

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Yu, X., and S. Beucher. "Vehicles Recognition by Video Camera." IFAC Proceedings Volumes 27, no. 12 (1994): 389–94. http://dx.doi.org/10.1016/s1474-6670(17)47501-x.

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Rodriguez, Julian Severiano. "A comparison of an RGB-D cameras performance and a stereo camera in relation to object recognition and spatial position determination." ELCVIA Electronic Letters on Computer Vision and Image Analysis 20, no. 1 (2021): 16–27. http://dx.doi.org/10.5565/rev/elcvia.1238.

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Results of using an RGB-D camera (Kinect sensor) and a stereo camera, separately, in order to determine the 3D real position of characteristic points of a predetermined object in a scene are presented. KAZE algorithm was used to make the recognition, that algorithm exploits the nonlinear scale space through nonlinear diffusion filtering; 3D coordinates of the centroid of a predetermined object were calculated employing the camera calibration information and the depth parameter provided by a Kinect sensor and a stereo camera. Experimental results show it is possible to get the required coordina

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Son, Sungho, and Han-Cheol Ryu. "Study on the Effect of Small Blockage on Autonomous Camera Recognition." Institute of Future Society and Christianity 4, no. 2 (2023): 89–99. http://dx.doi.org/10.53665/isc.4.2.89.

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The environmental awareness sensors of self-driving cars include cameras, radars, and lidar. While various cognitive sensors are employed in combinations as per self-driving manufacturers' unique development strategies, the camera is a consistently utilized sensor. Camera sensors are the only ones capable of capturing texture, color, and contrast information, as well as recognizing objects such as road lanes, signals, signs, pedestrians, bicycles, and surrounding vehicles. Due to the ever-increasing pixel resolution and relatively low prices, camera sensors are gaining importance in autonomous

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Fang, Liang, Zhiwei Guan, and Jinghua Li. "Automatic Roadblock Identification Algorithm for Unmanned Vehicles Based on Binocular Vision." Wireless Communications and Mobile Computing 2021 (November 23, 2021): 1–7. http://dx.doi.org/10.1155/2021/3333754.

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In order to improve the accuracy of automatic obstacle recognition algorithm for driverless vehicles, an automatic obstacle recognition algorithm for driverless vehicles based on binocular vision is constructed. Firstly, the relevant parameters of the camera are calibrated around the new car coordinate system to determine the corresponding obstacle position of the vehicle. At the same time, the three-dimensional coordinates of obstacle points are obtained by binocular matching method. Then, the left and right cameras are used to capture the feature points of obstacles in the image to realize t

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ABAYOMI-ALLI, A., E. O. OMIDIORA, S. O. OLABIYISI, J. A. Ojo, and A. Y. AKINGBOYE. "BLACKFACE SURVEILLANCE CAMERA DATABASE FOR EVALUATING FACE RECOGNITION IN LOW QUALITY SCENARIOS." Journal of Natural Sciences Engineering and Technology 15, no. 2 (2017): 13–31. http://dx.doi.org/10.51406/jnset.v15i2.1668.

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Many face recognition algorithms perform poorly in real life surveillance scenarios because they were tested with datasets that are already biased with high quality images and certain ethnic or racial types. In this paper a black face surveillance camera (BFSC) database was described, which was collected from four low quality cameras and a professional camera. There were fifty (50) random volunteers and 2,850 images were collected for the frontal mugshot, surveillance (visible light), surveillance (IR night vision), and pose variations datasets, respectively. Images were taken at distance 3.4,

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Jiang, Mingjun, Zihan Zhang, Kohei Shimasaki, Shaopeng Hu, and Idaku Ishii. "Multi-Thread AI Cameras Using High-Speed Active Vision System." Journal of Robotics and Mechatronics 34, no. 5 (2022): 1053–62. http://dx.doi.org/10.20965/jrm.2022.p1053.

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In this study, we propose a multi-thread artificial intelligence (AI) camera system that can simultaneously recognize remote objects in desired multiple areas of interest (AOIs), which are distributed in a wide field of view (FOV) by using single image sensor. The proposed multi-thread AI camera consists of an ultrafast active vision system and a convolutional neural network (CNN)-based ultrafast object recognition system. The ultrafast active vision system can function as multiple virtual cameras with high spatial resolution by synchronizing exposure of a high-speed camera and movement of an

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Bin Mohamad Azhar, Muhammad Darwish, Kah Ong Michael Goh, Law Check Yee, and Tee Connie. "A Robust License Plate Detection System Using Smart Device." JOIV : International Journal on Informatics Visualization 8, no. 2 (2024): 931. http://dx.doi.org/10.62527/joiv.8.2.2287.

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The license plate recognition (LPR) system is widely employed in various applications. However, most research studies have used a fixed camera rather than a moving one. This is because the location of the vehicle plate is nearly static and easily estimated, making the use of a static camera simple for locating and detecting the scanned license plate. Images obtained with a moving camera are highly complex due to frequent background changes. Additionally, a challenge with car plates in Malaysia is their non-standardized nature. Car owners are permitted to use any font type for their license pla

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Jianbo Zhang, Qun Yin, Duan Peng-Fei, and Meisu Yin. "Student Attendance Analysis and Statistics Platform based on Capture Recognition Technology." Electrotehnica, Electronica, Automatica 70, no. 1 (2022): 85–94. http://dx.doi.org/10.46904/eea.22.70.1.1108009.

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With the development of face recognition technology and HD camera, it is possible to use face recognition to realize the classroom attendance statistics. The traditional way of classroom attendance statics needs teachers to roll call according to the list of students, but face recognition can not only save the time of class, but also lighten the burden of statistics attendance of school. This paper realizes the face recognition system of attendance analysis and statistics platform, and it needs cameras and a main computer. In software, under the development environment of VS2017, it relies on

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Ramakic, Adnan, Zlatko Bundalo, and Zeljko Vidovic. "Feature extraction for person gait recognition applications." Facta universitatis - series: Electronics and Energetics 34, no. 4 (2021): 557–67. http://dx.doi.org/10.2298/fuee2104557r.

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In this paper we present some features that may be used in person gait recognition applications. Gait recognition is an interesting way of people identification. During a gait cycle, each person creates unique patterns that can be used for people identification. Also, gait recognition methods ordinarily do not need interaction with a person and that is the main advantage of these methods. Features used in a person gait recognition methods can be obtained with widely available RGB and RGB-D cameras. In this paper we present a two features which are suitable for use in gait recognition applicati

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Son, Sungho, Woongsu Lee, Hyungi Jung, et al. "Evaluation of Camera Recognition Performance under Blockage Using Virtual Test Drive Toolchain." Sensors 23, no. 19 (2023): 8027. http://dx.doi.org/10.3390/s23198027.

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This study is the first to develop technology to evaluate the object recognition performance of camera sensors, which are increasingly important in autonomous vehicles owing to their relatively low price, and to verify the efficiency of camera recognition algorithms in obstruction situations. To this end, the concentration and color of the blockage and the type and color of the object were set as major factors, with their effects on camera recognition performance analyzed using a camera simulator based on a virtual test drive toolkit. The results show that the blockage concentration has the la

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Aragon, Maria Christina, Melissa Juanillo, and Rosmina Joy Cabauatan. "Camera-Captured Writing System Recognition of Logosyllabic Han Character." International Journal of Computer and Communication Engineering 3, no. 3 (2014): 166–71. http://dx.doi.org/10.7763/ijcce.2014.v3.313.

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Dr, Vivek Gujar. "Artificial Intelligence, AI Camera: Market & About." Journal of Emerging Technologies and Innovative Research 10, no. 9 (2023): d738—d752. https://doi.org/10.5281/zenodo.10521195.

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Artificial Intelligence (AI) is revolutionizing the camera industry, with AI-powered cameras making significant strides in smartphones, surveillance, automotive, and healthcare. These cameras use machine learning algorithms to enhance image quality, enable advanced features, and automate tasks. In smartphones, AI cameras offer features like portrait and night mode. In surveillance, they provide smart monitoring and object tracking. In the automotive sector, AI cameras enhance safety through advanced driver-assistance systems (ADAS). Healthcare benefits from AI cameras in medical image analysis

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Betta, Giovanni, Domenico Capriglione, Mariella Corvino, et al. "Metrological characterization of 3D biometric face recognition systems in actual operating conditions." ACTA IMEKO 6, no. 1 (2017): 33. http://dx.doi.org/10.21014/acta_imeko.v6i1.392.

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<p>Nowadays, face recognition systems are going to widespread in many fields of application, from automatic user login for financial activities and access to restricted areas, to surveillance for improving security in airports and railway stations, to cite a few.<br />In such scenarios, the architectures based on stereo vision and 3D reconstruction of the face are going to assume a predominant role because they can generally assure a better reliability than solutions based on a single camera (which make use of a single image instead of a couple of images). To realize such systems,

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Satybaldina, D. Zh, N. S. Glazyrina, V. S. Stepanov, and K. A. Kalymova. "Development of a Python application for recognizing gestures from a video stream of RGB and RGBD cameras." Bulletin of L.N. Gumilyov Eurasian National University. Mathematics. Computer Science. Mechanics Series 136, no. 3 (2022): 6–17. http://dx.doi.org/10.32523/bulmathenu.2021/3.1.

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Gesture recognition systems have changed a lot recently, due to the development of modern data capture devices (sensors) and the development of new recognition algorithms. The article presents the results of a study for recognizing static and dynamic hand gestures from a video stream from RGB and RGBD cameras, namely from the Logitech HD Pro Webcam C920 webcam and from the Intel RealSense D435 depth camera. Software implementation is done using Python 3.6 tools. Open source Python libraries provide robust implementations of image processing and segmentation algorithms. The feature extraction a

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Afonso, Ana L., Gil Lopes, and A. Fernando Ribeiro. "Lizard Body Temperature Acquisition and Lizard Recognition Using Artificial Intelligence." Sensors 24, no. 13 (2024): 4135. http://dx.doi.org/10.3390/s24134135.

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The acquisition of the body temperature of animals kept in captivity in biology laboratories is crucial for several studies in the field of animal biology. Traditionally, the acquisition process was carried out manually, which does not guarantee much accuracy or consistency in the acquired data and was painful for the animal. The process was then switched to a semi-manual process using a thermal camera, but it still involved manually clicking on each part of the animal’s body every 20 s of the video to obtain temperature values, making it a time-consuming, non-automatic, and difficult process.

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Manoliu, Mitica-Valentin. "Biometric security: Recognition according to the pattern of palm veins." Scientific Bulletin of Naval Academy XXIII, no. 1 (2020): 257–62. http://dx.doi.org/10.21279/1454-864x-20-i1-036.

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Palm vein recognition is a promising new biometric method, which has additional potential in the forensic field. This process is performed using light using NIR(Near-infrared) LEDs and the camera that captures the acquisition of veins. The obtained images have noise with variations of rotation and translation. Therefore, the input image made by the camera must be pre-processed using characteristic processes. A set of features is extracted based on images taken from infrared light cameras and processed in order to make authentication possible. This whole process can be accomplished by several m

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Koo, Ja, Se Cho, Na Baek, Min Kim, and Kang Park. "CNN-Based Multimodal Human Recognition in Surveillance Environments." Sensors 18, no. 9 (2018): 3040. http://dx.doi.org/10.3390/s18093040.

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In the current field of human recognition, most of the research being performed currently is focused on re-identification of different body images taken by several cameras in an outdoor environment. On the other hand, there is almost no research being performed on indoor human recognition. Previous research on indoor recognition has mainly focused on face recognition because the camera is usually closer to a person in an indoor environment than an outdoor environment. However, due to the nature of indoor surveillance cameras, which are installed near the ceiling and capture images from above i

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Bharathi, M., N. Padmaja, and M. Dharani. "OCR-based vehicle number plate recognition powered by a raspberry Pi." i-manager’s Journal on Electronics Engineering 12, no. 3 (2022): 33. http://dx.doi.org/10.26634/jele.12.3.18959.

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Modern technology has revolutionized automation. Security is at high priority with increasing automation. Today, to help people feel comfortable, video surveillance cameras are installed in public places like schools, hospitals, and other buildings. The main goal of this research work is to automatically collect vehicle images with a camera using a Raspberry Pi and recognising the licence plate of the vehicles. Vehicle number plate recognition is a challenging but crucial system. This is highly helpful for automating toll booths, identifying automated signal violators, and identifying traffic

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Alsadik, Bashar, Luuk Spreeuwers, Farzaneh Dadrass Javan, and Nahuel Manterola. "Mathematical Camera Array Optimization for Face 3D Modeling Application." Sensors 23, no. 24 (2023): 9776. http://dx.doi.org/10.3390/s23249776.

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Camera network design is a challenging task for many applications in photogrammetry, biomedical engineering, robotics, and industrial metrology, among other fields. Many driving factors are found in the camera network design including the camera specifications, object of interest, and type of application. One of the interesting applications is 3D face modeling and recognition which involves recognizing an individual based on facial attributes derived from the constructed 3D model. Developers and researchers still face difficulty in reaching the required high level of accuracy and reliability n

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Wu, Yi-Chang, Yao-Cheng Liu, and Ru-Yi Huang. "Real-time microreaction recognition system." IAES International Journal of Robotics and Automation (IJRA) 12, no. 2 (2023): 157. http://dx.doi.org/10.11591/ijra.v12i2.pp157-166.

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<span lang="EN-US">This study constructed a real-time microreaction recognition system that can give real-time assistance to investigators. Test results indicated that the number of frames per second (30 or 190); angle of the camera, namely the front view of the interviewee or left (+45°) or right (−45°) view; and image resolution (480 or 680 p) did not have major effects on the system’s recognition ability. However, when the camera was placed at a distance of 300 cm, recognition did not always succeed. Value changes were larger when the camera was placed at an elevation 45° than when it

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Yi-Chang, Wu, Liu Yao-Cheng, and Huang Ru-Yi. "Real-time microreaction recognition system." IAES International Journal of Robotics and Automation (IJRA) 12, no. 2 (2023): 157–66. https://doi.org/10.11591/ijra.v12i2.pp157-166.

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This study constructed a real-time microreaction recognition system that can give real-time assistance to investigators. Test results indicated that the number of frames per second (30 or 190); angle of the camera, namely the front view of the interviewee or left (+45°) or right (−45°) view; and image resolution (480 or 680 p) did not have major effects on the system’s recognition ability. However, when the camera was placed at a distance of 300 cm, recognition did not always succeed. Value changes were larger when the camera was placed at an elevation 45° than when it

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Jang, Youjin, Inbae Jeong, Moein Younesi Heravi, Sajib Sarkar, Hyunkyu Shin, and Yonghan Ahn. "Multi-Camera-Based Human Activity Recognition for Human–Robot Collaboration in Construction." Sensors 23, no. 15 (2023): 6997. http://dx.doi.org/10.3390/s23156997.

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As the use of construction robots continues to increase, ensuring safety and productivity while working alongside human workers becomes crucial. To prevent collisions, robots must recognize human behavior in close proximity. However, single, or RGB-depth cameras have limitations, such as detection failure, sensor malfunction, occlusions, unconstrained lighting, and motion blur. Therefore, this study proposes a multiple-camera approach for human activity recognition during human–robot collaborative activities in construction. The proposed approach employs a particle filter, to estimate the 3D h

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Nishikawa, Noboru, Masaki Onishi, Takuya Matsumoto, Masao Izumi, and Kunio Fukunaga. "Object Recognition Based on Camera Control." IEEJ Transactions on Electronics, Information and Systems 118, no. 2 (1998): 210–16. http://dx.doi.org/10.1541/ieejeiss1987.118.2_210.

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Routray, Jyotirmayee, Sarthak Rout, Jiban Jyoti Panda, Bhabani Shankar Mohapatra, and Hitendrita Panda. "Hand Gesture Recognition using TOF camera." International Journal of Applied Engineering Research 16, no. 4 (2021): 302. http://dx.doi.org/10.37622/ijaer/16.4.2021.302-307.

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Kuznetsova, S. Yu, K. Zhigalov, and I. M. Daudov. "Camera testing technique for auto recognition." Journal of Physics: Conference Series 1582 (July 2020): 012058. http://dx.doi.org/10.1088/1742-6596/1582/1/012058.

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Berstis, Viktors. "Digital camera with voice recognition annotation." Journal of the Acoustical Society of America 116, no. 3 (2004): 1332. http://dx.doi.org/10.1121/1.1809943.

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Bhalerao, Prof R. S. "Sign Language Recognition." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6349–55. https://doi.org/10.22214/ijraset.2025.71693.

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This paper presents a novel real-time sign language detection system designed to enhance communication between the deaf and hard-of-hearing community and non-signers. Utilizing standard web cameras, the system captures and analyses hand and facial gestures, employing advanced computer vision and deep learning techniques to recognize sign language gestures. Key markers corresponding to specific signs are identified and translated into voice output and on-screen text, providing a dualoutput feature that fosters inclusivity and accessibility. By enabling real-time interpretation through voice and

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Rajarathinam, Robin Jephthah, Chris Palaguachi, and Jina Kang. "360-Degree Cameras vs Traditional Cameras in Multimodal Learning Analytics: Comparative Study of Facial Recognition and Pose Estimation." Journal of Educational Data Mining 17, no. 1 (2025): 157–82. https://doi.org/10.5281/zenodo.14966499.

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Multimodal Learning Analytics (MMLA) has emerged as a powerful approach within the computer-supported collaborative learning community, offering nuanced insights into learning processes through diverse data sources. Despite its potential, the prevalent reliance on traditional instruments such as tripod-mounted digital cameras for video capture often results in suboptimal data quality for facial expressions and poses captured, which is crucial for understanding collaborative dynamics. This study introduces an innovative approach to overcome this limitation by employing 360-degree camera technol

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B L, Sunil Kumar, and Sharmila Kumari M. "RGB-D FACE RECOGNITION USING LBP-DCT ALGORITHM." Applied Computer Science 17, no. 3 (2021): 73–81. http://dx.doi.org/10.35784/acs-2021-22.

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Face recognition is one of the applications in image processing that recognizes or checks an individual's identity. 2D images are used to identify the face, but the problem is that this kind of image is very sensitive to changes in lighting and various angles of view. The images captured by 3D camera and stereo camera can also be used for recognition, but fairly long processing times is needed. RGB-D images that Kinect produces are used as a new alternative approach to 3D images. Such cameras cost less and can be used in any situation and any environment. This paper shows the face recognition

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Chaudhari, V. J. "Currency Recognition App." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 435–37. http://dx.doi.org/10.22214/ijraset.2021.34982.

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Visually Impaired & foreign people are those people who have vision impairment or vision loss. Problems faced by visually impaired in performing daily activities are in great number. They also face a lot of difficulties in monetary transactions. They are unable to recognize the paper currencies due to similarity of paper texture and size between different categories. This money detector app helps visually impaired patients to recognize and detect money. Using this application blind people can speak and give command to open camera of a smartphone and camera will click picture of the note an

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Varga, Jozef, and Marek Sukop. "Simple Algorithm for Patterns Recognition." Applied Mechanics and Materials 844 (July 2016): 75–78. http://dx.doi.org/10.4028/www.scientific.net/amm.844.75.

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This article describes algorithm for patterns recognition of application for dual arm robot, android device and camera system. As first was create android application for getting information from computer via Bluetooth. Computer is using for image processing from external camera and then send image of dice to android devices and show on screen score of dice.

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