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On Detection, Data Association and Segmentation for Multi-Target Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019In this work, we propose a tracker that differs from most existing multi-target trackers in two major ways. First, our tracker does not rely on a pre-trained object detector to get the initial object hypotheses.
Yicong Tian, Afshin Dehghan, M. Shah
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Target Identification and Tracking
2013In this chapter we first recall the notion of contracted GPTs. Then we show that the CGPTs have some nice properties, such as simple rotation and translation formulas, simple relation with shape symmetry, etc. More importantly, we derive new invariants for the CGPTs. Based on those invariants, we develop a dictionary matching algorithm. We suppose that
Knut Sølna +6 more
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1996
Protein analysis and purification is aimed at elucidating the structure and function of proteins. In order to analyze a protein, you must have a reliable detection system that unambiguously enables you to follow the target protein. This is especially true when the target molecule is in crude or even semipurified form. Often, analytical results will lay
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Protein analysis and purification is aimed at elucidating the structure and function of proteins. In order to analyze a protein, you must have a reliable detection system that unambiguously enables you to follow the target protein. This is especially true when the target molecule is in crude or even semipurified form. Often, analytical results will lay
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2016
Multi-model algorithms have proven effective when the target is not cooperative. A hybrid tracker can tailor its kinematic-sensor model to the current conditions of the engagement. Unfortunately, in applications the correct model is seldom known with certainty.
David D. Sworder, John E. Boyd
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Multi-model algorithms have proven effective when the target is not cooperative. A hybrid tracker can tailor its kinematic-sensor model to the current conditions of the engagement. Unfortunately, in applications the correct model is seldom known with certainty.
David D. Sworder, John E. Boyd
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Benchmarks for target tracking
SPIE Proceedings, 2011The term benchmark originates from the chiseled horizontal marks that surveyors made, into which an angle-iron could be placed to bracket ("bench") a leveling rod, thus ensuring that the leveling rod can be repositioned in exactly the same place in the future.
Philip D. West, Darin T. Dunham
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2006 Proceeding of the Thrity-Eighth Southeastern Symposium on System Theory, 2006
Tracking moving targets is one of the important tasks in autonomous systems such as security and surveillance systems and unmanned aerial vehicles (UAV). When the camera system is stationery, background subtraction is a commonly used technique for segmenting out moving objects in a scene.
S.S. Polmottawegedara +2 more
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Tracking moving targets is one of the important tasks in autonomous systems such as security and surveillance systems and unmanned aerial vehicles (UAV). When the camera system is stationery, background subtraction is a commonly used technique for segmenting out moving objects in a scene.
S.S. Polmottawegedara +2 more
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Computer Vision and Pattern Recognition, 2019
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of ...
Zheng Tang +8 more
semanticscholar +1 more source
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of ...
Zheng Tang +8 more
semanticscholar +1 more source
2006 Fortieth Asilomar Conference on Signals, Systems and Computers, 2006
Model-based trackers generate a synthetic distribution function for the kinematic state of the target. The mean is used to locate the target, and the covariance is used to generate a confidence region about the mean. We shall show that a map-enhanced, multiple model algorithms can be used to reduce the tracking error and to make the confidence region ...
Robert J. Elliott +3 more
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Model-based trackers generate a synthetic distribution function for the kinematic state of the target. The mean is used to locate the target, and the covariance is used to generate a confidence region about the mean. We shall show that a map-enhanced, multiple model algorithms can be used to reduce the tracking error and to make the confidence region ...
Robert J. Elliott +3 more
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SPIE Proceedings, 2012
Steady-state performance of a tracking filter is traditionally evaluated immediately after a track update. However, there is commonly a further delay (e.g., processing and communications latency) before the tracks can actually be used. We analyze the accuracy of extrapolated target tracks for four tracking filters: Kalman filter with the Singer ...
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Steady-state performance of a tracking filter is traditionally evaluated immediately after a track update. However, there is commonly a further delay (e.g., processing and communications latency) before the tracks can actually be used. We analyze the accuracy of extrapolated target tracks for four tracking filters: Kalman filter with the Singer ...
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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 ...
Chun Chen +5 more
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