People-tracking-by-detection and people-detection-by-tracking

Both detection and tracking people are challenging problems, especially in complex real world scenes that commonly involve multiple people, complicated occlusions, and cluttered or even moving backgrounds. People detectors have been shown to be able to locate pedestrians even in complex street scene...

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Vydáno v:2008 IEEE Conference on Computer Vision and Pattern Recognition s. 1 - 8
Hlavní autoři: Andriluka, M., Roth, S., Schiele, B.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.06.2008
Témata:
Cameras
Computer science
Detectors
Gaussian processes
Hidden Markov models
Image sequences
Indexing
Layout
Legged locomotion
Surveillance
ISBN:9781424422425, 1424422426
ISSN:1063-6919, 1063-6919
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Abstract Both detection and tracking people are challenging problems, especially in complex real world scenes that commonly involve multiple people, complicated occlusions, and cluttered or even moving backgrounds. People detectors have been shown to be able to locate pedestrians even in complex street scenes, but false positives have remained frequent. The identification of particular individuals has remained challenging as well. Tracking methods are able to find a particular individual in image sequences, but are severely challenged by real-world scenarios such as crowded street scenes. In this paper, we combine the advantages of both detection and tracking in a single framework. The approximate articulation of each person is detected in every frame based on local features that model the appearance of individual body parts. Prior knowledge on possible articulations and temporal coherency within a walking cycle are modeled using a hierarchical Gaussian process latent variable model (hGPLVM). We show how the combination of these results improves hypotheses for position and articulation of each person in several subsequent frames. We present experimental results that demonstrate how this allows to detect and track multiple people in cluttered scenes with reoccurring occlusions.
AbstractList Both detection and tracking people are challenging problems, especially in complex real world scenes that commonly involve multiple people, complicated occlusions, and cluttered or even moving backgrounds. People detectors have been shown to be able to locate pedestrians even in complex street scenes, but false positives have remained frequent. The identification of particular individuals has remained challenging as well. Tracking methods are able to find a particular individual in image sequences, but are severely challenged by real-world scenarios such as crowded street scenes. In this paper, we combine the advantages of both detection and tracking in a single framework. The approximate articulation of each person is detected in every frame based on local features that model the appearance of individual body parts. Prior knowledge on possible articulations and temporal coherency within a walking cycle are modeled using a hierarchical Gaussian process latent variable model (hGPLVM). We show how the combination of these results improves hypotheses for position and articulation of each person in several subsequent frames. We present experimental results that demonstrate how this allows to detect and track multiple people in cluttered scenes with reoccurring occlusions.
Author Roth, S.
Schiele, B.
Andriluka, M.
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  organization: Comput. Sci. Dept., Tech. Univ. Darmstadt, Darmstadt
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Snippet Both detection and tracking people are challenging problems, especially in complex real world scenes that commonly involve multiple people, complicated...
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SubjectTerms Cameras
Computer science
Detectors
Gaussian processes
Hidden Markov models
Image sequences
Indexing
Layout
Legged locomotion
Surveillance
Title People-tracking-by-detection and people-detection-by-tracking
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