The demand for reliable traffic sign recognition
(TSR) increases with the development of safety driven advanced
driver assistance systems (ADAS). Emerging technologies
like brake-by-wire or steer-by-wire pave the way for collision
avoidance and threat identification systems. Obviously, decision
making in such critical situations requires high reliability of the
information base. Especially for comfort systems, we need to take
into account that the user tends to trust the information provided
by the ADAS.
In this paper, we present a robust system architecture for the
reliable recognition of circular traffic signs. Our system employs
complementing approaches for the different stages of current
TSR systems. This introduces the application of local SIFT
features for content-based traffic sign detection along with widely
applied shape-based approaches. We further add a technique
called contracting curve density (CCD) to refine the localization
of the detected traffic sign candidates and therefore increase
the performance of the subsequent classification module. Finally,
the recognition stage based on SIFT and SURF descriptions
of the candidates executed by a neural net provides a robust
classification of structured image content like traffic signs. By
applying these steps we compensate the weaknesses of the utilized
approaches, and thus, improve the system’s performance.
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