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Analysis of pedestrian collision risk using fuzzy inference model

The aim of this work is to improve driver awareness by proposing a collision risk analysis method. Pedestrian in the scene is observed by sequential frames from monocular camera mounted on the car. Positional information of object is extracted by projecting the centroid of bounding box on the ground plane. Four elements of collision criteria are constructed which are pedestrian walking direction, its velocity, car speed and relative distance of pedestrian. The analysis of collision risk is performed using fuzzy inference method that is used for calculating the degree of risk. Furthermore, localization of pedestrian is performed according to its risk score. The pedestrian with low collision score is labeled as low risk (green), pedestrian which is increasing its collision score is considered as medium risk (yellow) and pedestrian with high collision score is labelled as high risk (red). A quantitative analysis is performed by measuring effectiveness of this approach. The performance evaluation shows our proposed method achieved average accuracy 87.5% and it significantly outperforms human perception with more than 25% improvement.