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Tracking multiple objects in 3D
Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289), 2003A system for tracking multiple targets in 3D is described. The system is made up of two pan-and-tilt units that are attached to the extremities of a rotating arm. This configuration has several advantages and can deal with several specific instances of tracking more than one target.
João Pedro Barreto 0001 +3 more
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Multiple object tracking with extended occlusions
Quarterly Journal of Experimental Psychology, 2022In everyday life, we often view objects through a limited aperture (e.g., soccer players on TV or cars slipping into our blind spot on a busy road), where objects often move out of view and reappear in a different place later. We modelled this situation in a series of multiple object tracking (MOT) experiments, in which we introduced a cover on the ...
Jiří Lukavský +2 more
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Recursive Clustering for Multiple Object Tracking
2006 International Conference on Image Processing, 2006In this paper, we propose a method to track multiple deformable objects in video sequences using a recursive clustering scheme. In a first step, a set of Gabor filter banks is used to filter the difference image between two consecutive frames. Then, the moving areas are sampled by randomly positioning particles in high magnitude area of the filtered ...
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Segmentation and tracking of multiple video objects
Pattern Recognition, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Andrea Colombari +2 more
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A survey on multiple object tracking algorithm
2016 IEEE International Conference on Information and Automation (ICIA), 2016Visual Multiple Object Tracking (VMOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. There are many different approaches have been proposed to solve the problem. Compared with single object tracking which focuses on appearance model, motion model and other factors, multiple ...
Litong Fan +9 more
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Object tracking using multiple fragments
2009 16th IEEE International Conference on Image Processing (ICIP), 2009This paper presents a low-cost tracking algorithm based on multiple multiple fragments, increasing robustness with respect to partial occlusions. Given the initial template representing the desired target, each pixel is classified into a different cluster based on a Mixture of Gaussians (MOG) model, and a set of disjoint fragments is created.
Cláudio Rosito Jung, Amir Said
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Generic multiple object tracking
2015Multiple object tracking is an important problem in the computer vision community due to its applications, including but not limited to, visual surveillance, crowd behavior analysis and robotics. The difficulties of this problem lie in several challenges such as frequent occlusion, interaction, high-degree articulation, etc.
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Graph Networks for Multiple Object Tracking
2020 IEEE Winter Conference on Applications of Computer Vision (WACV), 2020Multiple object tracking (MOT) task requires reasoning the states of all targets and associating these targets in a global way. However, existing MOT methods mostly focus on the local relationship among objects and ignore the global relationship. Some methods formulate the MOT problem as a graph optimization problem. However, these methods are based on
Jiahe Li 0001 +2 more
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An algorithm for multiple object trajectory tracking
Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004Most tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework called Hidden Markov Model, where the distribution of the object state at current time instance is estimated based on current and previous observations.
Mei Han +3 more
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Multiple Feature Fusion for Object Tracking
2012In this paper, we propose a novel object tracking method by fusing multiple features. The tracking task is formulated under Bayesian inference framework. The posterior probability is resolved by the sum of weighted likelihood observations. Graph based semi-supervised learning method is used for likelihood evaluation, and the distance between foreground
Yu Zhou 0025 +4 more
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