We present a new method for obstacle and pedestrian detection with lower computation complexity and higher detection performance on a small target. By calculating stereo disparity according to the road parameters, our system efficiently detects objects above the ground. Experiments on over 10,000 images captured in urban areas demonstrate our method's effectiveness. We also propose a multi-scale compatible pedestrian detector. Detection speed is improved by avoiding size adjustment of input. Experiments on three public pedestrian databases and our HENU database show that our detector achieves better results than the state-of-the-art detection quality at equal speed, especially on pedestrian target with small size.