A Decision Fusion and Reasoning Module for a Traffic Sign Recognition System

A novel approach for a decision fusion and reasoning system for vision-based traffic sign recognition is presented. This module consists of several steps. In the first stage, a track-based Bayesian fusion scheme is used to fuse the classification results from each frame to obtain a fusion result for...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2011-12, Vol.12 (4), p.1126-1134
Main Authors: Meuter, M., Nunn, C., Gormer, S. M., Muller-Schneiders, S., Kummert, A.
Format: Article
Language:English
Subjects:
Active safety
Applied sciences
Artificial intelligence
Bayesian methods
Computer science
control theory
systems
decision fusion
Decision theory. Utility theory
Decision trees
Drivers
evaluation
Exact sciences and technology
Failure
Fuses
Image classification
image processing
Modules
moving host
Operational research and scientific management
Operational research. Management science
Pattern recognition. Digital image processing. Computational geometry
Reasoning
Recognition
Tracking
traffic sign recognition
Traffic signs
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Summary:A novel approach for a decision fusion and reasoning system for vision-based traffic sign recognition is presented. This module consists of several steps. In the first stage, a track-based Bayesian fusion scheme is used to fuse the classification results from each frame to obtain a fusion result for each track to decide whether a sign is present, as well as to determine the sign type. In order to determine the sign type, the temporal fusion scheme has been combined with a decision tree. In the second stage, the system combines and fuses probable identical objects which help to further reduce failures in the recognition process. The decision is based on the fusion results, as well as a position cue. Finally, a reasoning module is used to decide which of the passed signs should be shown to the driver. In addition to these modules, a general evaluation method for multi-class tracking systems is shown. While some failures are observed from the evaluation on object level, the additional post processing steps improve the system in such a way that the finally presented signs are almost always correct on the test set.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2011.2157497