Depth and Image Fusion for Road Obstacle Detection Using Stereo Camera
This addresses safety in autonomous driving or surveillance by detecting small, unknown obstacles, but it is incremental as it combines existing methods like SLIC segmentation with depth and RGB fusion.
The paper tackled road obstacle detection under unknown object appearance and varying conditions by fusing depth and image data from a stereo camera, demonstrating successful detection and tracking of small objects like dropped boxes in experiments.
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.