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Relevant bibliographies by topics / Objects tracking

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Academic literature on the topic 'Objects tracking'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Objects tracking"

1

Horowitz, Todd S., Sarah B. Klieger, David E. Fencsik, Kevin K. Yang, George A. Alvarez, and Jeremy M. Wolfe. "Tracking unique objects." Perception & Psychophysics 69, no. 2 (2007): 172–84. http://dx.doi.org/10.3758/bf03193740.

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Horowitz, T. S., Randall S. Birnkrant, Jeremy M. Wolfe, L. Tran, and David E. Fencsik. "Tracking invisible objects." Journal of Vision 4, no. 8 (2004): 366. http://dx.doi.org/10.1167/4.8.366.

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Mahler, Ronald P. S., and Vasileios Maroulas. "Tracking spawning objects." IET Radar, Sonar & Navigation 7, no. 3 (2013): 321–31. http://dx.doi.org/10.1049/iet-rsn.2012.0053.

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Kalaidzidis, Yannis. "Intracellular objects tracking." European Journal of Cell Biology 86, no. 9 (2007): 569–78. http://dx.doi.org/10.1016/j.ejcb.2007.05.005.

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Kaushik, Bhavana, Manoj Kumar, Charul Bhatanagar, and Anand Singh Jalal. "A Context Based Tracking for Similar and Deformable Objects." International Journal of Computer Vision and Image Processing 8, no. 4 (2018): 1–15. http://dx.doi.org/10.4018/ijcvip.2018100101.

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Object detection and tracking is the most basic and important process to understand video content. Due to its numerous applications, it has attracted many researchers to the field. Lots of work has been done in tracking; however, tracking of a deformable object is still in its initial stage. Most of the start-of-art methods fail to handle rapid variations in an object's shape and size. The task becomes even more difficult when there is multi-target tracking. In this article, the authors have proposed a novel tracking method based on context (spatio-temporal) formed from the corner points of ob

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Corcoran, Padraig. "Topology Based Object Tracking." Mathematical and Computational Applications 24, no. 3 (2019): 84. http://dx.doi.org/10.3390/mca24030084.

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A model for tracking objects whose topological properties change over time is proposed. Such changes include the splitting of an object into multiple objects or the merging of multiple objects into a single object. The proposed model employs a novel formulation of the tracking problem in terms of homology theory whereby 0-dimensional homology classes, which correspond to connected components, are tracked. A generalisation of this model for tracking spatially close objects lying in an ambient metric space is also proposed. This generalisation is particularly suitable for tracking spatial-tempor

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Killingsworth, S., and D. Levin. "Goal Objects Reduce Accuracy in Multiple Object Tracking." Journal of Vision 12, no. 9 (2012): 549. http://dx.doi.org/10.1167/12.9.549.

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Li, Dong Mei, Tao Li, Tao Xiang, and Wei Xu. "Multiple Objects Tracking Based on Linear Fitting." Applied Mechanics and Materials 602-605 (August 2014): 1438–41. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1438.

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For multiple objects tracking in complex scenes, a new tracking algorithm based on linear fitting for multiple moving objects is proposed. DG_CENTRIST feature and color feature are combined to describe the object, and the overlapping ratio of the tracking object is calculated. The object in the current frame is measured by using coincidence degree. If there is occlusion, we predict the path of each object by linear fitting and adjust the results of tracking in order to get correct results. The experiment results show that this method can effectively improve the accuracy of the multiple target

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Wang, Wenhao, Mingxin Jiang, Xiaobing Chen, Li Hua, and Shangbing Gao. "A Tracking Window Adaptive Compressive Tracking Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 14 (2019): 1959045. http://dx.doi.org/10.1142/s0218001419590456.

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In the original compression tracking algorithm, the size of the tracking box is fixed. There should be better tracking results for scale-invariant objects, but worse tracking results for scale-variant objects. To overcome this defect, a scale-adaptive compressive tracking (CT) algorithm is proposed. First of all, the imbalance of the gray and texture features in the original CT algorithm is balanced by the multi-feature method, which makes the algorithm more robust. Then, searching different candidate regions by using the method of multi-scale search along with feature normalization makes the

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Szinte, Martin, Marisa Carrasco, Patrick Cavanagh, and Martin Rolfs. "Attentional trade-offs maintain the tracking of moving objects across saccades." Journal of Neurophysiology 113, no. 7 (2015): 2220–31. http://dx.doi.org/10.1152/jn.00966.2014.

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In many situations like playing sports or driving a car, we keep track of moving objects, despite the frequent eye movements that drastically interrupt their retinal motion trajectory. Here we report evidence that transsaccadic tracking relies on trade-offs of attentional resources from a tracked object's motion path to its remapped location. While participants covertly tracked a moving object, we presented pulses of coherent motion at different locations to probe the allocation of spatial attention along the object's entire motion path. Changes in the sensitivity for these pulses showed that

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Dissertations / Theses on the topic "Objects tracking"

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Buchanan, Aeron Morgan. "Tracking non-rigid objects in video." Thesis, University of Oxford, 2008. http://ora.ox.ac.uk/objects/uuid:82efb277-abc9-4725-9506-5d114a83bd96.

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Video is a sequence of 2D images of the 3D world generated by a camera. As the camera moves relative to the real scene and elements of that scene themselves move, correlated frame-to-frame changes in the video images are induced. Humans easily identify such changes as scene motion and can readily assess attempts to quantify it. For a machine, the identification of the 2D frame-to-frame motion is difficult. This problem is addressed by the computer vision process of tracking. Tracking underpins the solution to the problem of augmenting general video sequences with artificial imagery, a staple t

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Dunne, Peter John. "Tracking moving objects in surveillance video." Thesis, University of Central Lancashire, 2012. http://clok.uclan.ac.uk/7129/.

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The thesis looks at approaches to the detection and tracking of potential objects of interest in surveillance video. The aim was to investigate and develop methods that might be suitable for eventual application through embedded software, running on a fixed-point processor, in analytics capable cameras. The work considers common approaches to object detection and representation, seeking out those that offer the necessary computational economy and the potential to be able to cope with constraints such as low frame rate due to possible limited processor time, or weak chromatic content that can o

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Sethuraman, Vijayashankar. "A hybrid technique for tracking network structured multiple deformable objects tracking." Thesis, University of Nottingham, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.546700.

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Ergezer, Hamza. "Visual Detection And Tracking Of Moving Objects." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/2/12609098/index.pdf.

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In this study, primary steps of a visual surveillance system are presented: moving object detection and tracking of these moving objects. Background subtraction has been performed to detect the moving objects in the video, which has been taken from a static camera. Four methods, frame differencing, running (moving) average, eigenbackground subtraction and mixture of Gaussians, have been used in the background subtraction process. After background subtraction, using some additional operations, such as morphological operations and connected component analysis, the objects to be tracked have been

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WESIERSKI, Daniel. "Visual tracking of articulated and flexible objects." Phd thesis, Institut National des Télécommunications, 2013. http://tel.archives-ouvertes.fr/tel-00939073.

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Humans can visually track objects mostly effortlessly. However, it is hard for a computer to track a fast moving object under varying illumination and occlusions, in clutter, and with varying appearance in camera projective space due to its relaxed rigidity or change in viewpoint. Since a generic, precise, robust, and fast tracker could trigger many applications, object tracking has been a fundamental problem of practical importance since the beginnings of computer vision. The first contribution of the thesis is a computationally efficient approach to tracking objects of various shapes and mot

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Djambazian, Haig. "3D tracking of non-rigid articulated objects." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33966.

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Articulated objects can be found in many living beings. Tracking is essential if we want to interpret the behavior of such objects. This thesis describes a framework for learning the relationship between the state and the appearance of the object. It will also show how to use this representation to track the state of the articulated object.<br>The learning phase of the method results in populations of models that describe the appearance of small regions of the object for small regions of the state space. To efficiently train off-line, it is necessary to model the appearance of the object as fu

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Endresen, Kai Hugo Hustoft. "Tracking objects in 3D using Stereo Vision." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9129.

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<p>This report describes a stereo vision system to be used on a mobile robot. The system is able to triangulate the positions of cylindrical and spherical objects in a 3D environment. Triangulation is done in real-time by matching regions in two images, and calculating the disparities between them.</p>

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Senthamil, Nalin Pradeep. "Accurate tracking of objects using level sets." Connect to this title online, 2009. http://etd.lib.clemson.edu/documents/1252937516/.

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Wesierski, Daniel. "Visual tracking of articulated and flexible objects." Thesis, Evry, Institut national des télécommunications, 2013. http://www.theses.fr/2013TELE0007/document.

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Les humains sont capables de suivre visuellement des objets sans effort. Cependant les algorithmes de vision artificielle rencontrent des limitations pour suivre des objets en mouvement rapide, sous un éclairage variable, en présence d'occultations, dans un environnement complexe ou dont l'apparence varie à cause de déformations et de changements de point de vue. Parce que des systèmes génériques, précis, robustes et rapides sont nécessaires pour de nombreuses d’applications, le suivi d’objets reste un problème pratique important en vision par ordinateur. La première contribution de cette thès

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Leung, Po. "Appearance-based tracking of non-rigid objects." Thesis, Queen Mary, University of London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500021.

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Books on the topic "Objects tracking"

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Lepetit, Vincent. Monocular model-based 3D tracking of rigid objects. NOW Publishers, 2005.

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Merritt, Paul. Video tracking of objects on an enhanced PC system. Naval Postgraduate School, 1989.

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Fundamentals of object tracking. Cambridge University Press, 2011.

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Mohith, S. Sudir. Real-time interactive object tracking. UMIST, 1998.

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Roth, Daniel Eugen. Real-time multi-object tracking. Hartung-Gorre, 2010.

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Streit, Roy, Robert Blair Angle, and Murat Efe. Analytic Combinatorics for Multiple Object Tracking. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61191-0.

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Han, Jinsong, Wei Xi, Kun Zhao, and Zhiping Jiang. Device-Free Object Tracking Using Passive Tags. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12646-3.

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1968-, Brown Paul, and Moore Dorothy, eds. Object-relational DBMSs: Tracking the next great wave. 2nd ed. Morgan Kaufmann Publishers, 1999.

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Karasulu, Bahadir. Performance Evaluation Software: Moving Object Detection and Tracking in Videos. Springer New York, 2013.

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Ramin, Kurt P., and Cornelis A. Reiman, eds. IFRS and XBRL: How to Improve Business Reporting Through Technology and Object Tracking. John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781119208099.

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Book chapters on the topic "Objects tracking"

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Meng, Xiaofeng, Zhiming Ding, and Jiajie Xu. "Moving Objects Tracking." In Moving Objects Management. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-38276-5_3.

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Peters, Gabriele, Christian Eckes, and Christoph von der Malsburg. "Tracking of rotating objects." In Advances in Pattern Recognition. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0033258.

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Zhou, Xingyi, Vladlen Koltun, and Philipp Krähenbühl. "Tracking Objects as Points." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58548-8_28.

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Koch, Wolfgang. "Characterizing Objects and Sensors." In Tracking and Sensor Data Fusion. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39271-9_2.

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Comport, Andrew I., Éric Marchand, and François Chaumette. "Complex Articulated Object Tracking." In Articulated Motion and Deformable Objects. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30074-8_19.

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Kolodner, Elliot K. "Concurrent Tracking of Stable Objects." In Persistent Object Systems. Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-3209-7_24.

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Zheng, Nanning, and Jianru Xue. "Bayesian Tracking of Visual Objects." In Statistical Learning and Pattern Analysis for Image and Video Processing. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-312-9_8.

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Güting, Ralf Hartmut. "Moving Objects Databases and Tracking." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_223.

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Güting, Ralf Hartmut. "Moving Objects Databases and Tracking." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_223.

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Güting, Ralf Hartmut. "Moving Objects Databases and Tracking." In Encyclopedia of Database Systems. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4899-7993-3_223-2.

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Conference papers on the topic "Objects tracking"

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Guler, Sadiye, Jason A. Silverstein, and Ian H. Pushee. "Stationary objects in multiple object tracking." In 2007 IEEE Conference on Advanced Video and Signal Based Surveillance. IEEE, 2007. http://dx.doi.org/10.1109/avss.2007.4425318.

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Pahwa, Ramanpreet Singh, Tian Tsong Ng, and Minh N. Do. "Tracking objects using 3D object proposals." In 2017 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC). IEEE, 2017. http://dx.doi.org/10.1109/apsipa.2017.8282298.

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Kim, Dal Hyung, Edward Steager, and Min Jun Kim. "Comparison of Contour Based and Feature Based Tracking Methods for Control of Microbiorobots." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-10564.

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Miniature robots should be precisely controlled because of a small workspace and size of their shapes. Small error of control could lead to failure of tasks such as an assembly. Tracking is one of the most important techniques because control of a small scale robot is hard to accomplish without object’s motion information. In this paper, we compare the feature based and the region based tracking methods with microbiorobot. Invariant features can be extracted using Scale Invariant Feature Transfrom (SIFT) algorithm because microbiorobot is a rigid body unlike a cell. We clearly showed that the

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Khurshid, Aasim, and Jacob Scharcanski. "Adaptive Face Tracking Based on Online Learning." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8297.

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Object tracking can be used to localize objects in scenes, and also can be used for locating changes in the object’s appearance or shape over time. Most of the available object tracking methods tend to perform satisfactorily in controlled environments but tend to fail when the objects appearance or shape changes, or even when the illumination changes (e.g., when tracking non-rigid objects such as a human face). Also, in many available tracking methods, the tracking error tends to increase indefinitely when the target is missed. Therefore, tracking the target objects in long and uninterrupted v

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Bermudez, Francely Franco, Christian Santana Diaz, Sheneeka Ward, Rafael Radkowski, Timothy Garrett, and James Oliver. "Comparison of Natural Feature Descriptors for Rigid-Object Tracking for Real-Time Augmented Reality." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35319.

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This paper presents a comparison of natural feature descriptors for rigid object tracking for augmented reality (AR) applications. AR relies on object tracking in order to identify a physical object and to superimpose virtual object on an object. Natural feature tracking (NFT) is one approach for computer vision-based object tracking. NFT utilizes interest points of a physcial object, represents them as descriptors, and matches the descriptors against reference descriptors in order to identify a phsical object to track. In this research, we investigate four different natural feature descriptor

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Tena Ruiz, I. "Tracking objects in underwater multibeam sonar images." In IEE Colloquium on Motion Analysis and Tracking. IEE, 1999. http://dx.doi.org/10.1049/ic:19990581.

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Curila, Sorin, Cornelia E. Gordan, and Mircea Curila. "Tracking of polyhedral objects." In 2008 International Conference on Intelligent Computer Communication and Processing (ICCP). IEEE, 2008. http://dx.doi.org/10.1109/iccp.2008.4648355.

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Aswani, Parimal, K. K. Wong, and Man N. Chong. "Tracking of deformable objects." In Photonics West 2001 - Electronic Imaging, edited by Bernd Girod, Charles A. Bouman, and Eckehard G. Steinbach. SPIE, 2000. http://dx.doi.org/10.1117/12.411824.

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Jiang, Hao, and Mark S. Drew. "Tracking objects with shadows." In Electronic Imaging 2003, edited by Bhaskaran Vasudev, T. Russell Hsing, Andrew G. Tescher, and Touradj Ebrahimi. SPIE, 2003. http://dx.doi.org/10.1117/12.476458.

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Radkowski, Rafael, Timothy Garrett, Jarid Ingebrand, and David Wehr. "TrackingExpert: A Versatile Tracking Toolbox for Augmented Reality." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60401.

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This paper presents a toolbox for rigid object tracking with a focus on augmented reality applications. Augmented reality relies on tracking to superimpose virtual objects on physical objects. Object tracking is usually based on registration and pose estimation techniques. Many different approaches have already been introduced. Our research focuses on tracking for application areas such as assembly assistance and the most promising candidate is rigid object tracking based on point cloud registration. Our work advances the robustness of point cloud-based tracking as well as the performance. One

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Reports on the topic "Objects tracking"

1

Aggarwal, J. K. Automatic Recognition and Tracking of Objects. Defense Technical Information Center, 1985. http://dx.doi.org/10.21236/ada177008.

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Bartesaghi, Alberto, and Guillermo Sapiro. Tracking of Moving Objects Under Severe and Total Occlusions. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada519184.

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Magomedov, I. A., and I. S. Timergeriev. TRACKING OBJECTS IN VIDEO FLOW BASED ON CONVOLUTIONARY NEURAL NETWORKS. PJSC GAZPROM, 2019. http://dx.doi.org/10.18411/9785-6043-2019-25557.

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Dutra, Lauren M., James Nonnemaker, Nathaniel Taylor, et al. Visual Attention to Tobacco-Related Stimuli in a 3D Virtual Store. RTI Press, 2020. http://dx.doi.org/10.3768/rtipress.2020.rr.0036.2005.

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We used eye tracking to measure visual attention to tobacco products and pro- and anti-tobacco advertisements (pro-ads and anti-ads) during a shopping task in a three-dimensional virtual convenience store. We used eye-tracking hardware to track the percentage of fixations (number of times the eye was essentially stationary; F) and dwell time (time spent looking at an object; DT) for several categories of objects and ads for 30 adult current cigarette smokers. We used Wald F-tests to compare fixations and dwell time across categories, adjusting comparisons of ads by the number of each type of a

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Amphay, Sengvieng, and David Gray. Infrared Stationary Object Acquisition and Moving Object Tracking. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada538516.

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Gyaourova, A., C. Kamath, and S. Cheung. Block Matching for Object Tracking. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15009731.

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Magomedov, I. A., and I. S. Timergeriev. TRACKING OBJECT MOTION SPEED IN VIDEO FLOW. PJSC GAZPROM, 2019. http://dx.doi.org/10.18411/9785-6043-2019-25860.

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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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Godil, Afzal, Roger Eastman, and Tsai Hong Hong. Ground truth systems for object recognition and tracking. National Institute of Standards and Technology, 2013. http://dx.doi.org/10.6028/nist.ir.7923.

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Balcells Capellades, Marti. An Appearance Based Approach for Human and Object Tracking. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada459305.

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