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Feature-based probabilistic data association and tracking

: Grinberg, M.; Ohr, F.; Willersinn, D.; Beyerer, J.

Volltext urn:nbn:de:0011-n-1341344 (2.2 MByte PDF)
MD5 Fingerprint: 0f2c9e91c98f9ca1ee7a1c420779977b
Erstellt am: 24.6.2010

Rohling, H. ; TU Hamburg-Harburg, Institut für Nachrichtentechnik:
7th International Workshop on Intelligent Transportation, WIT 2010. Proceedings : Hamburg, Germany, March 23rd and 24th, 2010
Hamburg, 2010
International Workshop on Intelligent Transportation (WIT) <7, 2010, Hamburg>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
environment perception; featurebased; multitarget tracking; track-before-detect; probabilistic data association; point tracking; 6D-vision; split and merge handling; occlusion handling; stereo video

In this contribution we present a concept for improvement of object tracking in applications that suffer from severe detection errors such as incomplete, merged, split, missing and clutter-based detections due to noisy data, sensory and algorithmic restrictions and occlusions. It is based on utilization of low-level information that is gained through tracking dedicated feature points with known relationship to the tracked objects. The proposed Feature-Based Probabilistic Data Association and Tracking Algorithm (FBPDA) can be applied not only in the field of driver assistance systems but also in surveillance applications and further video-based object tracking applications. The main requirement is the possibility to robustly track dedicated feature points in the image (and in 3D space). For this aim, both correlation-based techniques (optic flow) and correspondence-based techniques using e.g. SIFT or SURF features can be used.