engleski

Addressing false alarms and localization inaccuracy in traffic sign detection and recognition

We present a study on applying Viola-Jones detection and SVM classification for recognizing traffic signs in video. Extensive experimentation has shown that this combination suffers from high incidence of false alarms and low tolerance to localization inaccuracy of the true positive detection responses. We report on three improvements which effectively alleviate these problems. Firstly, we confirm the previous result that raw detection performance of Viola-Jones detector can be improved by exploiting color. Additionally, we propose a solution for filtering false positive detection responses, based on a properly trained artificial neural network classifier in the last stage of the detection cascade. Finally, we pro pose a novel approach for alleviating the degradation of the classification performance due to localization inaccuracy. Experiments have been performed on several video sequences acquired from a moving vehicle, containing several hundred triangular warning signs. The results indicate a dramatic improvement in detection precision, as well as significant improvements in classification performance. At the system level, the proposed system correctly classified more than 97% of triangular warning signs, while producing only a few false alarms in more than 130000 image frames.

Object detection; object recognition; machine learning; traffic signs.

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