Klinger, Tobias; Rottensteiner, Franz; Heipke, Christian : Probabilistic multi-person tracking using dynamic bayes networks. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences II-3/W5 (2015), S. 435-442. DOI:
https://doi.org/10.5194/isprsannals-ii-3-w5-435-2015
Zusammenfassung: |
Tracking-by-detection is a widely used practice in recent tracking systems. These usually rely on independent single frame detections that are handled as observations in a recursive estimation framework. If these observations are imprecise the generated trajectory is prone to be updated towards a wrong position. In contrary to existing methods our novel approach uses a Dynamic Bayes Network in which the state vector of a recursive Bayes filter, as well as the location of the tracked object in the image are modelled as unknowns. These unknowns are estimated in a probabilistic framework taking into account a dynamic model, and a state-of-the-art pedestrian detector and classifier. The classifier is based on the Random Forest-algorithm and is capable of being trained incrementally so that new training samples can be incorporated at runtime. This allows the classifier to adapt to the changing appearance of a target and to unlearn outdated features. The approach is evaluated on a publicly available benchmark. The results confirm that our approach is well suited for tracking pedestrians over long distances while at the same time achieving comparatively good geometric accuracy.
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Lizenzbestimmungen: |
CC BY 3.0 Unported - https://creativecommons.org/licenses/by/3.0/
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Publikationstyp: |
Article |
Publikationsstatus: |
publishedVersion |
Erstveröffentlichung: |
2015 |
Schlagwörter (englisch): |
Recursion, Machine learning, Trajectory, Tracking system, Artificial intelligence, Bayes' theorem, State vector, Computer vision, Bayesian network, Recursive Bayesian estimation, Computer science, Probabilistic logic
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Fachliche Zuordnung (DDC): |
550 | Geowissenschaften
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Kontrollierte Schlagwörter: |
Konferenzschrift
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