Generation of Roadside Panoramic Images
without Obstacles
Shunichiro Tsuji

Yasuyuki Kono

School of Science & Technology
Kwansei Gakuin University
2-1 Gakuen, Sanda, Japan

School of Science & Technology
Kwansei Gakuin University
2-1 Gakuen, Sanda, Japan

coi29394@kwansei.ac.jp

kono@kwansei.ac.jp

Categories and Subject Descriptors

I.4 [Image Processing and Computer Vision]: Applications;
H.5 [Information Interfaces and Presentation]: Multimedia
Information SystemsHypertext navigation and maps
General Terms

Design feature, Human Factor

Figure 1: In-vehicle camera images.

Keywords

SIFT feature, real-world map, panoramic image generation,
obstacle exclusion
1. INTRODUCTION

This paper presents the method for generating panoramic
image(s) from plural in-vehicle camera images. Our realworld map system employing the proposed method has thefollowing features: 1) it puts the generated panoramic images of the real-world to an existing web map, e.g., GoogleMaps,2) partial regions in the images are classied according to theFigure 2: A generated panoramic image.
depth from the camera by tracking each partial region employing the SIFT feature, so that 3) it can obtain panoramic
background images that exclude obstacles, e.g., parked vehi- The distance of corresponding feature points between twocles. Our updatable real-world map system can oer up-to- frame images reects the depth to the region they belong
date information to map-based systems, e.g., car navigation to, i.e., a feature point moves faster if it is nearer to thesystem or web interface any time.
camera. We can classify the projected objects in the captured images into similar depth regions by grouping feature
2. DEPTH-BASED CLASSIFICATIONpoints according to their movement speed. We employ theA monocular video camera is mounted in the center of the SIFT feature extraction method to correctly associate corleft side window of a test vehicle and faced towards the left responding feature points between frames.
side of the car. The in-vehicle camera captures the scenery of
the road side while driving. A GPS receiver is also mounted Figure 2 shows the panoramic image of the backgroundin the vehicle and each captured image is annotated by its buildings generated by unifying the slowest regions accordlocation information. Figure 1 shows sample images of every ing to the amount of movement of feature points. The uni5 frames. Two pickup trucks are parked on the roadside and cation is overall successful, although the system could not
occludes the background buildings as shown in Figure 1.
recover the region where the obstacles fully occluded. Also
some part of the background was misclassied as vehicle
Our idea is to cluster feature points into similar depth re- region and some of the vehicle as the background mainly
gions according to their movement speed between frames. around the border of the regions.
3.

FUTURE WORKS

This paper presented the method for generating panoramic
image by classifying regions in frame images according to theamount of movement. The method is simple and was overall successful. However, it is necessary to accurately divide
regions on the border of the subjects. Also to update thegenerated images by following the change of the real-world,
the system should detect the changes of the background.