Extended CoHOG and particle filter by improved motion model for pedestrian active safety

The percentage of pedestrian deaths in traffic accidents is on the rise. In recent years, there have been calls for measures to be introduced to protect such vulnerable road users as pedestrians and cyclists. In this study, a method to detect pedestrians using an in-vehicle camera is presented. We improved the technology in detecting pedestrians with highly accurate images using a monocular camera. We were able to predict pedestrians' activities by monitoring them, and we developed an algorithm to recognize pedestrians and their movements more accurately. The effectiveness of the algorithm was tested using images taken on real roads. For the feature descriptor, we found that an extended co-occurrence histogram of oriented gradients, accumulating the integration of gradient intensities. In tracking step, we applied effective motion model using optical flow for Particle Filter tracking. These techniques are valified by using images captured on the real road.