Vision algorithms and embedded solution for pedestrian detection with far infrared camera

In the automotive industry the issue of safety remains a major priority. This aspect is not focused just on the driver but also on the other participants of the traffic like the pedestrians. This paper describes a pedestrian detection system where three different classification methods are used for detecting pedestrians with a far infrared camera. The three methods are tested and compared on variable number of features in order to obtain a scalable solution. The authors propose a low cost embedded implementation for the classification method that has proven to be best with respect to the accuracy and training time, taking the HOG as features descriptors for the region of interest.