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2020
The classic Bayes-Markov (BM), probabilistic data association (PDA), and integrated PDA (IPDA) filters are derived using the analytic combinatorics (AC) method. The probability generating functional (GFL) of the BM filter is an integral, obtained as the limit of a Riemann sum of a discrete problem discussed in Chap. 1.
Roy Streit +2 more
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The classic Bayes-Markov (BM), probabilistic data association (PDA), and integrated PDA (IPDA) filters are derived using the analytic combinatorics (AC) method. The probability generating functional (GFL) of the BM filter is an integral, obtained as the limit of a Riemann sum of a discrete problem discussed in Chap. 1.
Roy Streit +2 more
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IEE Seminar on Railway Information Systems, 2004
Availability of the railway network is getting more and more crucial. This makes it increasingly important for maintenance contractors to prevent failures at the rail infrastructure that disturb the train service. In addition to this, improving maintenance efficiency remains an important point of interest.
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Availability of the railway network is getting more and more crucial. This makes it increasingly important for maintenance contractors to prevent failures at the rail infrastructure that disturb the train service. In addition to this, improving maintenance efficiency remains an important point of interest.
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Object tracking using mobile tracking stations
2015 IEEE Aerospace Conference, 2015Tracking mobile objects can present many challenges, especially when they are autonomous or semi-autonomous and may move unpredictably. Many of these issues can be mitigated using mobile tracking stations mounted on robots under the user's direct control.
Jasmine Cashbaugh +2 more
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3D Multi-Object Tracking: A Baseline and New Evaluation Metrics
IEEE/RJS International Conference on Intelligent RObots and Systems, 20193D multi-object tracking (MOT) is an essential component for many applications such as autonomous driving and assistive robotics. Recent work on 3D MOT focuses on developing accurate systems giving less attention to practical considerations such as ...
Xinshuo Weng +3 more
semanticscholar +1 more source
Selecting and tracking multiple objects
WIREs Cognitive Science, 2014When interacting with the world, people can dynamically split attention across multiple objects in the environment, both when the objects are stationary and when the objects are moving. This type of visual processing is commonly studied in lab settings using either static selection tasks or moving tracking tasks. We describe performance limits that are
Jason M. Scimeca, Steven L. Franconeri
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Tracking in object action space
Computer Vision and Image Understanding, 2013In this paper we focus on the joint problem of tracking humans and recognizing human action in scenarios such as a kitchen scenario or a scenario where a robot cooperates with a human, e.g., for a manufacturing task. In these scenarios, the human directly interacts with objects physically by using/manipulating them or by, e.g., pointing at them such as
Krüger, Volker, Herzog, Dennis
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2011
Maneuvering objects are those objects whose dynamical behavior changes over time. An object that suddenly turns or accelerates displays a maneuvering behavior with regard to its tracked position. While the definition of a maneuvering object extends beyond the tracking of position and speed, historically it is in this context that maneuvering object ...
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Maneuvering objects are those objects whose dynamical behavior changes over time. An object that suddenly turns or accelerates displays a maneuvering behavior with regard to its tracked position. While the definition of a maneuvering object extends beyond the tracking of position and speed, historically it is in this context that maneuvering object ...
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003
D. Comaniciu +2 more
semanticscholar +1 more source
D. Comaniciu +2 more
semanticscholar +1 more source
2016
We talked about how to obtain features of a sub-image, in the previous chapter. In this chapter, we will discuss the approach to obtain the bounding box of each candidates from a raw video frame and maintain consistent identification for each person in a video sequence, that is, multi-object tracking.
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We talked about how to obtain features of a sub-image, in the previous chapter. In this chapter, we will discuss the approach to obtain the bounding box of each candidates from a raw video frame and maintain consistent identification for each person in a video sequence, that is, multi-object tracking.
openaire +1 more source