Depth estimation for a road scene using a monocular image sequence based on fully convolutional neural network. (21st May 2020)

• Record Type: Journal Article
• Authors: Wang, Haixia; Sun, Yehao; Zhang, Zhiguo; Lu, Xiao; Sheng, Chunyang
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 3(2020:May/Jun.)
• Publication Date: 2020-05-21
• Subjects: Depth estimation -- monocular sequence -- fully convolutional neural network -- road scene; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420925305
• Languages: English
• ISSNs: 1729-8806

Abstract

An advanced driving assistant system is one of the most popular topics nowadays, and depth estimation is an important cue for advanced driving assistant system. Depth prediction is a key problem in understanding the geometry of a road scene for advanced driving assistant system. In comparison to other depth estimation methods using stereo depth perception, determining depth relation using a monocular camera is considerably challenging. In this article, a fully convolutional neural network with skip connection based on a monocular video sequence is proposed. With the integration framework that combines skip connection, fully convolutional network and the consistency between consecutive frames of the input sequence, high-resolution depth maps are obtained with lightweight network training and fewer computations. The proposed method models depth estimation as a regression problem and trains the proposed network using a scale invariance optimization based on L2 loss function, which measures the relationships between points in the consecutive frames. The proposed method can be used for depth estimation of a road scene without the need for any extra information or geometric priors. Experiments on road scene data sets demonstrate that the proposed approach outperforms previous methods for monocular depth estimation in dynamic scenes. Compared with the currently proposed method, our method has achieved good results when using the Eigen split evaluation method. The obvious prominent one is that the linear root mean squared error result is 3.462 and the δ < 1.25 result is 0.892.