Attention-based traffic sign recognition with an array of weak classifiers

Currently available traffic sign recognition systems typically focus on a single class of traffic sign and therefore, the algorithms are optimized to find only this specific class. To this end, a number of approaches for real time capable classification of mostly circular signs exist. Nevertheless, to simultaneously recognize a number of classes a different way has to be taken. This paper presents a real-time capable approach, which uses a two-tiered process independent of the diameter of the sign to cope with all distances. The first stage is our attention system, parameterized to find a number of different types of sign classes. The output of our attention system is a region of interest with a potential traffic sign candidate. The second stage is an array of weak classifiers similar to the idea of Viola and Jones, computing a probability value for each of the sign classes. As application area we focus on inner city and therefore, evaluate on the most important traffic sign classes of Stop and Give Way. Nevertheless, the approach can also detect Warning signs and is easily extensible to additional sign classes. The evaluation results show the reliability and mark it as first step towards an overall traffic sign recognition.