Pedestrian Detection Based on Deep Learning
Pedestrian Detection Based on Deep Learning
Jeon, Hyung Min and Nguyen, Vinh Dinh and Jeon, Jae Wook
IECON Proceedings (Industrial Electronics Conference) 2019
Abstract : While it is a hot issue whether cars could drive by themselves in emergent situations without any kind of human interference, pedestrian detection is the key technology in autonomous driving cars. Though current pedestrian detection technologies have come to a point in which they are accurate in normal conditions and surroundings, existent systems are inaccurate in harsh situations, such as when there are too many pedestrians, when there is too much light or when it is too dark, or when it is raining or snowing heavily. This problem may be solved by integrating deep learning and combining a new type of local pattern with the RGB raw image as input, instead of using just the RGB image as input. We will introduce a new type of local pattern called Triangular Patterns, which is effective in extracting more detailed and stable features from local regions. Here in this paper, we propose a pedestrian detection system in which deep learning is used, along with combining the RGB raw image with Triangular Patterns for input.
