Citation

Suhr JK, Jung HG. IEEE Trans. Intel. Transp. Syst. 2020; 21(1): 117-134.

Copyright

(Copyright © 2020, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2018.2889603

PMID

unavailable

Abstract

Backing crashes have become a serious problem, resulting in the United States announcing a rule requiring all vehicles to come equipped with rearview cameras. As a result of this situation, this paper proposes a method that detects obstacles behind a vehicle using a rearview camera with a fisheye lens. The proposed method represents obstacles by means of the Stixel World, and it consists of two stages: free space boundary detection and obstacle height estimation. In the first stage, obstacles are three-dimensionally reconstructed, and the 3D points are spatially interpolated and sequentially accumulated. An occupancy grid map is generated with depth and incidence angle axes to effectively handle a wide field-of-view situation. In the second stage, this method combines some complementary information to reliably estimate the obstacle height using spare 3D points. Three cost maps based on 3D points, color similarity, and edge strength are calculated and fused in height and incidence angle axes. The free space boundary and the obstacle height are estimated by finding the optimal paths in the occupancy grid map and the fused cost map, respectively. Finally, Stixels are generated by integrating the free space boundary and obstacle height. In the experiment, the proposed method was quantitatively evaluated and compared with the previous methods in various road, obstacle, and background conditions.

Language: en