Depth and Image Fusion for Road Obstacle Detection Using Stereo Camera

TL;DR

This paper presents a fusion method combining depth and image data from stereo cameras to detect small road obstacles in challenging conditions, such as variable lighting and surface textures.

Contribution

The authors developed a novel depth and image fusion technique that enhances obstacle detection without relying on ML/DL methods, suitable for unknown object features.

Findings

Successfully detected and tracked small obstacles in parking lot scenarios

Effective in conditions with variable illumination and surface textures

Demonstrated robustness for static and low-speed obstacles

Abstract

This paper is devoted to the detection of objects on a road, performed with a combination of two methods based on both the use of depth information and video analysis of data from a stereo camera. Since neither the time of the appearance of an object on the road, nor its size and shape is known in advance, ML/DL-based approaches are not applicable. The task becomes more complicated due to variations in artificial illumination, inhomogeneous road surface texture, and unknown character and features of the object. To solve this problem we developed the depth and image fusion method that complements a search of small contrast objects by RGB-based method, and obstacle detection by stereo image-based approach with SLIC superpixel segmentation. We conducted experiments with static and low speed obstacles in an underground parking lot and demonstrated the successful work of the developed technique for detecting and even tracking small objects, which can be parking infrastructure objects, things left on the road, wheels, dropped boxes, etc.