An improved 2D cost aggregation method for advanced driver assistance systems

In advanced driver assistance systems, the stereo matching algorithm is the key resource to obtain depth information of outdoor scenes. Semi-Global Matching (SGM) is currently the most efficient stereo matching algorithm for outdoor environments. However, because the number of pixels is large, SGM u...

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Bibliographic Details
Main Authors: Ha, JeongMok, Jeon, Byeongchan, Jun, WooYeol, Lee, JoonHo, Jeong, Hong
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Aggregates
Arrays
Computational complexity
Cost function
Measurement
Transforms
Online Access:Request full text
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Summary:In advanced driver assistance systems, the stereo matching algorithm is the key resource to obtain depth information of outdoor scenes. Semi-Global Matching (SGM) is currently the most efficient stereo matching algorithm for outdoor environments. However, because the number of pixels is large, SGM uses only a subset of them when estimating the disparity of a pixel. To overcome this limitation, Cost Aggregation Table (CAT) was proposed which uses two-dimensional cost aggregation so as to utilize whole image information. In this paper, we propose improved global 2D cost aggregation methods by loosening aggregation constraints. It aggregates every cost in the whole image to estimate each disparity. Although our method aggregates every cost in the image, the computational complexity is the same as that of SGM and CAT. The proposed cost aggregation method achieves superior disparity accuracy compared to the SGM.
ISSN:1931-0587
2642-7214
DOI:10.1109/IVS.2015.7225668