Feature correspondence using probabilistic data association

Authors:
Yi-Sheng Yao;Rama Chellappa
Affiliations:
Department of Electrical Engineering, University of Maryland, College Park, MD;Department of Electrical Engineering, University of Maryland, College Park, MD
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: image and multidimensional signal processing - Volume V
Year:
1993

Citing 3
Cited 0

Finding Trajectories of Feature Points in a Monocular Image Sequence

IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking and data association

Tracking and data association
Feature Point Correspondence in the Presence of Occlusion

IEEE Transactions on Pattern Analysis and Machine Intelligence

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Abstract

A complete algorithm for feature point correspondence of a long sequence of images is presented. First feature points are extracted from the first frame. Then based on a two-dimensional(2-D) constant translation and rotation model, an Extended Kalman Filter is applied to predict the location of the corresponding point. Matching is done by comparing the feature vector and a motion continuity measure. Track initiation and termination are handled by the Probabilistic Data Association Filter. A method for including new features before the termination of gradually unreliable trajectories is introduced.