Pedestrian detection with a resolution-aware convolutional network
Keiichi Yamada · 2016
Pedestrian detection from in-vehicle camera images for the purpose of advanced driver assistance systems is of particular importance in cases of low-resolution pedestrians, because it is desirable to detect the pedestrian as far from the vehicle as possible to effectively provide safe driving support for the driver. Most previous studies on pedestrian detection, however, have focused on pedestrians with comparatively high resolutions. Unfortunately, the scale invariant assumption does not hold in the case of low resolution, and the pedestrian detection performance suffers greatly as the resolution decreases. From this background, this paper deals with pedestrian detection from an image including low-resolution pedestrians. We present a method of detecting pedestrians with a resolution-aware CNN-based architecture, RACNN, that learns low-level image feature with resolution information. Furthermore, we demonstrate the advantage of the proposed method by the evaluation using the Caltech-USA dataset.