The continuous growth of imaging databases increasingly requires analysis tools for extraction of features. In this paper,
a new architecture for the detection of traffic signs is proposed. The architecture is designed to process a large database
with tens of millions of images with a resolution up to 4,800x2,400 pixels. Because of the size of the database, a high
reliability as well as a high throughput is required. The novel architecture consists of a three-stage algorithm with
multiple steps per stage, combining both color and specific spatial information. The first stage contains an area-limitation
step which is performance critical in both the detection rate as the overall processing time. The second stage locates
suggestions for traffic signs using recently published feature processing. The third stage contains a validation step to
enhance reliability of the algorithm. During this stage, the traffic signs are recognized. Experiments show a convincing
detection rate of 99%. With respect to computational speed, the throughput for line-of-sight images of 800×600 pixels is
35 Hz and for panorama images it is 4 Hz. Our novel architecture outperforms existing algorithms, with respect to both
detection rate and throughput
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