A vision based real-time rear obstacle detection system is one of the most essential technologies, which can be used in many applications such as a parking assistance systems and intelligent vehicles. Although disparity is a useful feature for detecting obstacles, estimating a correct disparity map is a hard problem due to the matching ambiguity and noise sensitivity, especially in homogeneous regions. To overcome these problems, we leverage reliable disparities only for obstacle detection. A reliability factor is introduced to measure an inhomogeneity of the regions quantitatively. It is computed at each superpixel to consider the noise sensitivity of pixel-wise gradients and to assign similar reliability value within a same object. It includes two major components: firstly, In a feature extraction and combining stage, we extract three features from stereo images such as disparity, superpixel segments and pixel-wise gradient and compute the reliability of disparity from superpixel segments and the pixel-wise gradient. Secondly, In an obstacle detection stage, a disparity feature with reliability votes for localizing obstacles and dominant candidates in voting map are selected as initial obstacle region. The initial obstacle regions are expanded into their neighbor superpixels based on CIELAB color similarity and distance similarity between superpixels. Experimental results show satisfactory performance under various real parking environments. Its detection rate is at least 4% higher than those of other existing methods, and its false detection rate is more than 10% lower and thus, can be used for parking assistance system.
Bibliographical noteFunding Information:
This work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government ( MSIP ) (No. R0115-15-1007 , High quality 2d-to-multiview contents generation from large-scale RGB+D database). The authors would like to thank to support of Hyundai Motors to construct Database.
© 2016 Elsevier Ltd. All rights reserved.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence