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Relevant bibliographies by topics / Machine vision; Object tracking

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Academic literature on the topic 'Machine vision; Object tracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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Journal articles on the topic "Machine vision; Object tracking"

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Patil, Rupali, Adhish Velingkar, Mohammad Nomaan Parmar, Shubham Khandhar, and Bhavin Prajapati. "Machine Vision Enabled Bot for Object Tracking." JINAV: Journal of Information and Visualization 1, no. 1 (2020): 15–26. http://dx.doi.org/10.35877/454ri.jinav155.

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Object detection and tracking are essential and testing undertaking in numerous PC vision appliances. To distinguish the object first find a way to accumulate information. In this design, the robot can distinguish the item and track it just as it can turn left and right position and afterward push ahead and in reverse contingent on the object motion. It keeps up the consistent separation between the item and the robot. We have designed a webpage that is used to display a live feed from the camera and the camera can be controlled by the user efficiently. Implementation of machine learning is do

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Llano, Christian R., Yuan Ren, and Nazrul I. Shaikh. "Object Detection and Tracking in Real Time Videos." International Journal of Information Systems in the Service Sector 11, no. 2 (2019): 1–17. http://dx.doi.org/10.4018/ijisss.2019040101.

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Object and human tracking in streaming videos are one of the most challenging problems in vision computing. In this article, we review some relevant machine learning algorithms and techniques for human identification and tracking in videos. We provide details on metrics and methods used in the computer vision literature for monitoring and propose a state-space representation of the object tracking problem. A proof of concept implementation of the state-space based object tracking using particle filters is presented as well. The proposed approach enables tracking objects/humans in a video, incl

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Zhang, Xiao Jing, Chen Ming Sha, and Ya Jie Yue. "A Fast Object Tracking Approach in Vision Application." Applied Mechanics and Materials 513-517 (February 2014): 3265–68. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3265.

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Object tracking has always been a hot issue in vision application, its application area include video surveillance, human-machine, virtual reality and so on. In this paper, we introduce the Mean shift tracking algorithm, which is a kind of important no parameters estimation method, then we evaluate the tracking performance of Mean shift algorithm on different video sequences.

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Liu, Liyun. "Moving Object Detection Technology of Line Dancing Based on Machine Vision." Mobile Information Systems 2021 (April 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/9995980.

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In this paper, line dancing's moving object detection technology based on machine vision is studied to improve object detection. For this purpose, the improved frame difference for the background modeling technique is combined with the target detection algorithm. The moving target is extracted, and the postmorphological processing is carried out to make the target detection more accurate. Based on this, the tracking target is determined on the time axis of the moving target tracking stage, the position of the target in each frame is found, and the most similar target is found in each frame of

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Wang, Yongqing, and Yanzhou Zhang. "OBJECT TRACKING BASED ON MACHINE VISION AND IMPROVED SVDD ALGORITHM." International Journal on Smart Sensing and Intelligent Systems 8, no. 1 (2015): 677–96. http://dx.doi.org/10.21307/ijssis-2017-778.

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Jun, Mao. "Object Detection and Recognition Algorithm of Moving UAV Based on Machine Vision." Journal of Computational and Theoretical Nanoscience 13, no. 10 (2016): 7731–37. http://dx.doi.org/10.1166/jctn.2016.5770.

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To conquer disadvantages of slow speed of target tracking algorithm in original distribution field as well as easiness of being caught in local optimal solution, one target tracking algorithm of real-time distribution field based on global matching is presented in the Thesis, thus remarkably improving performance of target tracking algorithm in distribution field. In proposed algorithm, relevant correlation coefficients will be used to substitute the similarity between target distribution filed of original L1 norm measurement and candidate distribution field. As a consequence, the target searc

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Zhang, Zheng, Cong Huang, Fei Zhong, Bote Qi, and Binghong Gao. "Posture Recognition and Behavior Tracking in Swimming Motion Images under Computer Machine Vision." Complexity 2021 (May 20, 2021): 1–9. http://dx.doi.org/10.1155/2021/5526831.

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This study is to explore the gesture recognition and behavior tracking in swimming motion images under computer machine vision and to expand the application of moving target detection and tracking algorithms based on computer machine vision in this field. The objectives are realized by moving target detection and tracking, Gaussian mixture model, optimized correlation filtering algorithm, and Camshift tracking algorithm. Firstly, the Gaussian algorithm is introduced into target tracking and detection to reduce the filtering loss and make the acquired motion posture more accurate. Secondly, an

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Akbari Sekehravani, Ehsan, Eduard Babulak, and Mehdi Masoodi. "Flying object tracking and classification of military versus nonmilitary aircraft." Bulletin of Electrical Engineering and Informatics 9, no. 4 (2020): 1394–403. http://dx.doi.org/10.11591/eei.v9i4.1843.

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Tracking of moving objects in a sequence of images is one of the important and functional branches of machine vision technology. Detection and tracking of a flying object with unknown features are important issues in detecting and tracking objects. This paper consists of two basic parts. The first part involves tracking multiple flying objects. At first, flying objects are detected and tracked, using the particle filter algorithm. The second part is to classify tracked objects (military or nonmilitary), based on four criteria; Size (center of mass) of objects, object speed vector, the directio

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Delforouzi, Ahmad, Bhargav Pamarthi, and Marcin Grzegorzek. "Training-Based Methods for Comparison of Object Detection Methods for Visual Object Tracking." Sensors 18, no. 11 (2018): 3994. http://dx.doi.org/10.3390/s18113994.

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Object tracking in challenging videos is a hot topic in machine vision. Recently, novel training-based detectors, especially using the powerful deep learning schemes, have been proposed to detect objects in still images. However, there is still a semantic gap between the object detectors and higher level applications like object tracking in videos. This paper presents a comparative study of outstanding learning-based object detectors such as ACF, Region-Based Convolutional Neural Network (RCNN), FastRCNN, FasterRCNN and You Only Look Once (YOLO) for object tracking. We use an online and offlin

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Aziz, Nor Nadirah Abdul, Yasir Mohd Mustafah, Amelia Wong Azman, et al. "Features-Based Moving Objects Tracking for Smart Video Surveillances: A Review." International Journal on Artificial Intelligence Tools 27, no. 02 (2018): 1830001. http://dx.doi.org/10.1142/s0218213018300016.

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Video surveillance is one of the most active research topics in the computer vision due to the increasing need for security. Although surveillance systems are getting cheaper, the cost of having human operators to monitor the video feed can be very expensive and inefficient. To overcome this problem, the automated visual surveillance system can be used to detect any suspicious activities that require immediate action. The framework of a video surveillance system encompasses a large scope in machine vision, they are background modelling, object detection, moving objects classification, tracking

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Dissertations / Theses on the topic "Machine vision; Object tracking"

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Case, Isaac. "Automatic object detection and tracking in video /." Online version of thesis, 2010. http://hdl.handle.net/1850/12332.

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Clarke, John Christopher. "Applications of sequence geometry to visual motion." Thesis, University of Oxford, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.244549.

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Tydén, Amanda, and Sara Olsson. "Edge Machine Learning for Animal Detection, Classification, and Tracking." Thesis, Linköpings universitet, Reglerteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166572.

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A research field currently advancing is the use of machine learning on camera trap data, yet few explore deep learning for camera traps to be run in real-time. A camera trap has the purpose to capture images of bypassing animals and is traditionally based only on motion detection. This work integrates machine learning on the edge device to also perform object detection. Related research is brought up and model tests are performed with a focus on the trade-off regarding inference speed and model accuracy. Transfer learning is used to utilize pre-trained models and thus reduce training time and

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Stigson, Magnus. "Object Tracking Using Tracking-Learning-Detection inThermal Infrared Video." Thesis, Linköpings universitet, Datorseende, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93936.

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Automatic tracking of an object of interest in a video sequence is a task that has been much researched. Difficulties include varying scale of the object, rotation and object appearance changing over time, thus leading to tracking failures. Different tracking methods, such as short-term tracking often fail if the object steps out of the camera’s field of view, or changes shape rapidly. Also, small inaccuracies in the tracking method can accumulate over time, which can lead to tracking drift. Long-term tracking is also problematic, partly due to updating and degradation of the object model, lea

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Patrick, Ryan Stewart. "Surveillance in a Smart Home Environment." Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1278508516.

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Moujtahid, Salma. "Exploiting scene context for on-line object tracking in unconstrained environments." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEI110/document.

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Avec le besoin grandissant pour des modèles d’analyse automatiques de vidéos, le suivi visuel d’objets est devenu une tache primordiale dans le domaine de la vision par ordinateur. Un algorithme de suivi dans un environnement non contraint fait face à de nombreuses difficultés: changements potentiels de la forme de l’objet, du fond, de la luminosité, du mouvement de la camera, et autres. Dans cette configuration, les méthodes classiques de soustraction de fond ne sont pas adaptées, on a besoin de méthodes de détection d’objet plus discriminantes. De plus, la nature de l’objet est a priori inco

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Skjong, Espen, and Stian Aas Nundal. "Tracking objects with fixed-wing UAV using model predictive control and machine vision." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-25990.

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This thesis describes the development of an object tracking system for unmanned aerial vehicles (UAVs), intended to be used for search and rescue (SAR) missions. The UAV is equipped with a two-axis gimbal system, which houses an infrared (IR) camera used to detect and track objects of interest, and a lower level autopilot. An external computer vision (CV) module is assumed implemented and connected to the object tracking system, providing object positions and velocities to the control system. The realization of the object tracking system includes the design and assembly of the UAVs paylo

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Adeboye, Taiyelolu. "Robot Goalkeeper : A robotic goalkeeper based on machine vision and motor control." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-27561.

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This report shows a robust and efficient implementation of a speed-optimized algorithm for object recognition, 3D real world location and tracking in real time. It details a design that was focused on detecting and following objects in flight as applied to a football in motion. An overall goal of the design was to develop a system capable of recognizing an object and its present and near future location while also actuating a robotic arm in response to the motion of the ball in flight. The implementation made use of image processing functions in C++, NVIDIA Jetson TX1, Sterolabs’ ZED stereosco

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Barkman, Richard Dan William. "Object Tracking Achieved by Implementing Predictive Methods with Static Object Detectors Trained on the Single Shot Detector Inception V2 Network." Thesis, Karlstads universitet, Fakulteten för hälsa, natur- och teknikvetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-73313.

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In this work, the possibility of realising object tracking by implementing predictive methods with static object detectors is explored. The static object detectors are obtained as models trained on a machine learning algorithm, or in other words, a deep neural network. Specifically, it is the single shot detector inception v2 network that will be used to train such models. Predictive methods will be incorporated to the end of improving the obtained models’ precision, i.e. their performance with respect to accuracy. Namely, Lagrangian mechanics will be employed to derived equations of motion fo

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Ozertem, Kemal Arda. "Vision-assisted Object Tracking." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614073/index.pdf.

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In this thesis, a video tracking method is proposed that is based on both computer vision and estimation theory. For this purpose, the overall study is partitioned into four related subproblems. The first part is moving object detection<br>for moving object detection, two different background modeling methods are developed. The second part is feature extraction and estimation of optical flow between video frames. As the feature extraction method, a well-known corner detector algorithm is employed and this extraction is applied only at the moving regions in the scene. For the feature points, th

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Books on the topic "Machine vision; Object tracking"

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IEEE Workshop on Multi-Object Tracking (2001 Vancouver, B.C.). Proceedings: 2001 IEEE workshop on multi-object tracking : July 8, 2001, Vancouver, British Columbia, Canada. IEEE Computer Society, 2001.

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Video segmentation and its applications. Springer, 2011.

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2001 IEEE Workshop on Multi-Object Tracking: Proceedings. Institute of Electrical & Electronics Enginee, 2001.

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Taylor, Geoffrey, and Lindsay Kleeman. Visual Perception and Robotic Manipulation: 3D Object Recognition, Tracking and Hand-Eye Coordination. Springer, 2014.

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J, Tarr Michael, and Bülthoff Heinrich H, eds. Object recognition in man, monkey, and machine. MIT Press, 1998.

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Loui, Alexander Chan Pong. A morphological approach to moving-object recognition with application to machine vision. 1990.

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Visual Perception and Robotic Manipulation: 3D Object Recognition, Tracking and Hand-Eye Coordination (Springer Tracts in Advanced Robotics). Springer, 2006.

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Chakraborty, Shouvik, and Kalyani Mali. Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities. IGI Global, 2020.

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Chakraborty, Shouvik, and Kalyani Mali. Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities. IGI Global, 2020.

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Chakraborty, Shouvik, and Kalyani Mali. Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities. IGI Global, 2020.

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Book chapters on the topic "Machine vision; Object tracking"

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Roberti, Flavio, Juan Marcos Toibero, Jorge A. Sarapura, Víctor Andaluz, Ricardo Carelli, and José María Sebastián. "Unified Passivity-Based Visual Control for Moving Object Tracking." In Machine Vision and Navigation. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22587-2_12.

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Nguyen, Hien Van, Amit Banerjee, Philippe Burlina, Joshua Broadwater, and Rama Chellappa. "Tracking and Identification via Object Reflectance Using a Hyperspectral Video Camera." In Machine Vision Beyond Visible Spectrum. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11568-4_9.

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Bhattacharya, Subhabrata, Haroon Idrees, Imran Saleemi, Saad Ali, and Mubarak Shah. "Moving Object Detection and Tracking in Forward Looking Infra-Red Aerial Imagery." In Machine Vision Beyond Visible Spectrum. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11568-4_10.

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Schlemmer, Matthias J., Georg Biegelbauer, and Markus Vincze. "An Integration Concept for Vision-Based Object Handling: Shape-Capture, Detection and Tracking." In Advances in Machine Vision, Image Processing, and Pattern Analysis. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11821045_23.

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Sánchez-Nielsen, Elena, and Mario Hernández-Tejera. "Tracking Deformable Objects with Evolving Templates for Real-Time Machine Vision." In Pattern Recognition, Machine Intelligence and Biometrics. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22407-2_9.

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Verghese, Gilbert, Karey Lynch Gale, and Charles R. Dyer. "Real-Time, Parallel Motion Tracking of Three Dimensional Objects From Spatiotemporal Sequences." In Parallel Algorithms for Machine Intelligence and Vision. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3390-9_9.

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Bourezak, Rafik, and Guillaume-Alexandre Bilodeau. "Iterative Division and Correlograms for Detection and Tracking of Moving Objects." In Advances in Machine Vision, Image Processing, and Pattern Analysis. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11821045_5.

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Davies, E. Roy. "Object Location Using the HOUGH Transform." In Machine Vision Handbook. Springer London, 2012. http://dx.doi.org/10.1007/978-1-84996-169-1_18.

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Batchelor, Bruce G. "Appendix F: Object Location and Orientation." In Machine Vision Handbook. Springer London, 2012. http://dx.doi.org/10.1007/978-1-84996-169-1_47.

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Zhang, Geng, Zejian Yuan, Nanning Zheng, Xingdong Sheng, and Tie Liu. "Visual Saliency Based Object Tracking." In Computer Vision – ACCV 2009. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12304-7_19.

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Conference papers on the topic "Machine vision; Object tracking"

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Zheng, Feng, Ling Shao, and James Brownjohn. "Learn++ for Robust Object Tracking." In British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.28.

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Yan, Wang, Xiaoye Han, and Vladimir Pavlovic. "Structured Learning for Multiple Object Tracking." In British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.48.

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Milletari, Fausto, Wadim Kehl, Federico Tombari, Slobodan Ilic, Seyed-Ahmad Ahmadi, and Nassir Navab. "Universal Hough dictionaries for object tracking." In British Machine Vision Conference 2015. British Machine Vision Association, 2015. http://dx.doi.org/10.5244/c.29.122.

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Donoser, M., and H. Bischof. "Fast Non-Rigid Object Boundary Tracking." In British Machine Vision Conference 2008. British Machine Vision Association, 2008. http://dx.doi.org/10.5244/c.22.1.

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Masson, L., F. Jurie, and M. Dhome. "Tracking 3D Object using Flexible Models." In British Machine Vision Conference 2005. British Machine Vision Association, 2005. http://dx.doi.org/10.5244/c.19.37.

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Gaidon, Adrien, and Eleonora Vig. "Online Domain Adaptation for Multi-Object Tracking." In British Machine Vision Conference 2015. British Machine Vision Association, 2015. http://dx.doi.org/10.5244/c.29.3.

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Pan, Jinshan, Jongwoo Lim, and Ming-Hsuan Yang. "L0-Regularized Object Representation for Visual Tracking." In British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.29.

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Javan Roshtkhari, Mehrsan, and Martin Levine. "Multiple Object Tracking Using Local Motion Patterns." In British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.92.

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Luo, Wenhan, and Tae-Kyun Kim. "Generic Object Crowd Tracking by Multi-Task Learning." In British Machine Vision Conference 2013. British Machine Vision Association, 2013. http://dx.doi.org/10.5244/c.27.73.

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Ye, Mingquan, Hong Chang, and Xilin Chen. "Online Visual Tracking via Coupled Object-Context Dictionary." In British Machine Vision Conference 2015. British Machine Vision Association, 2015. http://dx.doi.org/10.5244/c.29.165.

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Reports on the topic "Machine vision; Object tracking"

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Stamler, Zachary. Methods for Object Tracking With Machine Vision. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7507.

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