Real-time rear obstacle detection using reliable disparity for driver assistance. (1st September 2016)

• Record Type: Journal Article
• Authors: Yoo, Hunjae; Son, Jongin; Ham, Bumsub; Sohn, Kwanghoon
• Is Part Of: Expert systems with applications. Volume 56(2016)
• Page Start: 186
• Page End: 196
• Publication Date: 2016-09-01
• Subjects: Rear obstacle detection -- Stereo vision for obstacle detection -- Disparity error robust obstacle detection -- Disparity reliability via inhomogeneity of superpixel; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.02.049
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract : Hightlights: Utilizing three features such as disparity, super pixel segments and pixel-wise gradient. Computing the reliability of disparity from super pixel segments and pixel-wise gradient. Developing voting map to reduce time complexity of initial obstacle region. Superior performance with erroneous disparity information and in complex environments. Abstract: 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.