Robust feature descriptor and vehicle motion model with tracking-by-detection for active safety

Abstract

The percentage of pedestrian deaths in traffic accidents is on the rise in Japan. In recent years, there have been calls for measures to be introduced to protect vulnerable road users such as pedestrians and cyclists. In this study, a method to detect and track pedestrians using an in-vehicle camera is presented to perform braking controls, warn the driver, and develop improved safety systems for pedestrians. We improved the technology of detecting pedestrians using highly accurate images obtained with a monocular camera. We were able to predict pedestrian activity by monitoring the images, and developed an algorithm with which 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 used an extended co-occurrence histogram of oriented gradients (ECoHOG) that accumulated the integration of gradient intensities. In the tracking step, we applied an effective motion model using optical flow and the proposed feature descriptor ECoHOG in a tracking-by-detection framework. These techniques were verified using images captured on the real road.