Results 221 to 230 of about 289,900 (263)
Some of the next articles are maybe not open access.
2009
This chapter covers the following topics: track-while-scan; coherent pulsed tracking radar; limitations to moving target indicator (MTI) performance; range gated pulsed Doppler tracking; coordinate frames; antenna mounts and servo systems; on-axis tracking; and tracking in cartesian space.
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This chapter covers the following topics: track-while-scan; coherent pulsed tracking radar; limitations to moving target indicator (MTI) performance; range gated pulsed Doppler tracking; coordinate frames; antenna mounts and servo systems; on-axis tracking; and tracking in cartesian space.
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Backtracking: Retrospective multi-target tracking
Computer Vision and Image Understanding, 2012We introduce a multi-target tracking algorithm that operates on prerecorded video as typically found in post-incident surveillance camera investigation. Apart from being robust to visual challenges such as occlusion and variation in camera view, our algorithm is also robust to temporal challenges, in particular unknown variation in frame rate.
Koppen, W.P., Worring, M.
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2010
Multitarget tracking intensity filters are closely related to imaging problems, especially PET imaging. The intensity filter is obtained by three different methods. One is a Bayesian derivation involving target prediction and information updating. The second approach is a simple, compelling, and insightful intuitive argument.
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Multitarget tracking intensity filters are closely related to imaging problems, especially PET imaging. The intensity filter is obtained by three different methods. One is a Bayesian derivation involving target prediction and information updating. The second approach is a simple, compelling, and insightful intuitive argument.
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Conference Proceedings '88., IEEE Southeastcon, 2003
A decentralized tracking technique of a target in clutter is discussed. The coordinator must reconstruct the trajectory of a moving object based on the return histories of N geographically distributed sensors. The coordinator can only access the (local) statistics produced by local processing of the sensors' return histories, not the sensors' returns ...
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A decentralized tracking technique of a target in clutter is discussed. The coordinator must reconstruct the trajectory of a moving object based on the return histories of N geographically distributed sensors. The coordinator can only access the (local) statistics produced by local processing of the sensors' return histories, not the sensors' returns ...
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Learning to Track Multiple Targets
IEEE Transactions on Neural Networks and Learning Systems, 2015Monocular multiple-object tracking is a fundamental yet under-addressed computer vision problem. In this paper, we propose a novel learning framework for tracking multiple objects by detection. First, instead of heuristically defining a tracking algorithm, we learn that a discriminative structure prediction model from labeled video data captures the ...
Xiao, Liu +5 more
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2013
Multi-target tracking refers to sequential estimation of the number of targets and their states (positions, velocities, etc.) tagged by a unique label. Hence the output of a tracking algorithm are tracks, where a track represents a labeled temporal sequence of state estimates, associated with the same target.
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Multi-target tracking refers to sequential estimation of the number of targets and their states (positions, velocities, etc.) tagged by a unique label. Hence the output of a tracking algorithm are tracks, where a track represents a labeled temporal sequence of state estimates, associated with the same target.
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2018
We have proposed a tracker which is based on multiple hypothesis model and iterative closest point algorithm. The algorithm successfully tracks multiple targets by using multiple hypothesis model matching approach. Hypothesis models are formed by using track information; the models are updated periodically whenever a new track is added.
Kiran Phalke, Ravindra Hegadi
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We have proposed a tracker which is based on multiple hypothesis model and iterative closest point algorithm. The algorithm successfully tracks multiple targets by using multiple hypothesis model matching approach. Hypothesis models are formed by using track information; the models are updated periodically whenever a new track is added.
Kiran Phalke, Ravindra Hegadi
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Tracking TRALI in target populations
Blood, 2011A prospective, case-controlled study in cardiac surgery reveals a high incidence (2.4%) of TRALI despite the introduction of plasma from male donors, indicating a need for additional interventions in susceptible populations.
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2004
This chapter addresses the basic issues of tracking airborne targets (aircraft, missiles, and so on) with a radar operating in a track-while-scan mode. Although multitarget tracking radars are of primary interest, the issues associated with tracking a single target will be addressed first. These issues are addressed first because most multitarget track
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This chapter addresses the basic issues of tracking airborne targets (aircraft, missiles, and so on) with a radar operating in a track-while-scan mode. Although multitarget tracking radars are of primary interest, the issues associated with tracking a single target will be addressed first. These issues are addressed first because most multitarget track
openaire +1 more source