Vehicle Detection and Disparity Estimation Using Blended Stereo Images
Changxin Zhou, Yazhou Liu, Quansen Sun, Pongsak Lasang
Abstract
In this article, a new method is presented for simultaneous vehicle detection and disparity estimation from the blended multiple-view image. The multiple-view image is created by blending left and right images from a stereo camera. This blended image can implicitly encode the appearance and view disparity of an object in a single image, which provides clues for detection and disparity estimation. Since a single object may have multiple overlaid correspondence in a blended image, the detection task is formulated as finding a group of associated bounding boxes with similar appearances. The distances between the associated bounding boxes can be used to estimate the disparity of the object. The proposed method has following benefits: 1) The detection and disparity estimation tasks can be accomplished using a single input image; 2) Even the task is more complex than the popular detection task, the increase in computation cost is negligible; 3) Very competitive detection and disparity estimation accuracy is obtained, even the detection is performed on the blended image which is unfriendly to human vision. The experiment is conducted using KITTI stereo subset and very promising result has been observed.