Road target depth estimation method based on ground clue propagation
A target depth, ground technology, applied in computing, image data processing, instruments, etc., can solve problems such as unsatisfactory depth estimation results, insufficient global depth relationship, and inconsistency of actual depth information.
Active Publication Date: 2018-10-09
HEFEI UNIV OF TECH
View PDF5 Cites 6 Cited by
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
However, the global depth relationship obtained by using the layout structure and semantic information is not fine enough, and often does not match the actual depth information.
[0005] Aiming at the problem of unsatisfactory depth estimation results above, this paper proposes a road target depth estimation based on ground clue propagation.
Method used
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View moreImage
Smart Image Click on the blue labels to locate them in the text.
Smart ImageViewing Examples
Examples
Experimental program
Comparison scheme
Effect test
Embodiment Construction
[0149] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The present invention is a road target depth estimation based on ground clue propagation, and the specific process is as follows figure 1 As shown, the implementation scheme of the present invention is divided into the following steps:
[0150] Step S1: Extract over-segmented superpixels from the input road image, and combine them to generate a target area. The specific operation steps include:
[0151] Step S1-1: For the input image, use the watershed algorithm to obtain the over-segmented superpixel set SP, denoted as SP={sp i},i=1,...,n sp , where sp i is the superpixel numbered i, n sp is the number of superpixels in the road image;
[0152]Step S1-2: Calculate the average RGB color of all superpixels, and extract the average RGB color of all superpixels C={c i},i=1,...,n sp ;
[0153] Step S1-3: Mark all superpixels as untraversed, a...
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More PUM
Login to View More Abstract
The invention discloses a road target depth estimation method based on ground clue propagation. Depth inference and estimation of a scene target are realized through obtaining depth distribution of the vertical object scene in contact with the ground and utilizing the edge blocking orientation relationship among regions. Firstly, over-segmented super-pixels of a road image are obtained, the over-segmented super-pixels are traversed sequentially, and region merging is performed according to the RGB average color to generate a target region; secondly, vanishing points of and image surface marksof the road image are extracted, for the vertical object region, Gaussian probability distribution estimation is utilized to extract the depth of the segmentation region; thirdly, according to the shared boundary of the segmentation region, an edge blocking orientation detector is constructed, and the edge blocking relationship among the segmented regions is extracted; and lastly, ground clue propagation is utilized till the scene depth relationship is stable, the target depth is outputted, and scene target depth estimation is obtained.
Description
technical field [0001] The invention belongs to the technical field of scene depth estimation, and more particularly, relates to a road target depth estimation method based on ground clue propagation. Background technique [0002] The depth in the vision system is a measure of the vertical distance between the observed object and the imaging plane, and is an important 2.5-dimensional information representation that converts the two-dimensional plane captured by ordinary sensor equipment into a three-dimensional structure description. In computer vision, depth estimation helps guide humans to perform 3D reality simulation of scenes, and also has broad application prospects in road monitoring equipment, robot navigation and other fields. Due to the complexity of real road scenes, recovering the depth information of road objects from a single image presents multiple challenges. [0003] The monocular image depth information estimation mainly relies on some monocular depth cues...
Claims
the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More Application Information
Patent Timeline
Login to View More IPC IPC(8): G06T7/11G06T7/13G06T7/187G06T7/50G06T7/90
CPCG06T2207/10024G06T2207/20081G06T2207/30256G06T7/11G06T7/13G06T7/187G06T7/50G06T7/90
Inventor 张鹿鸣谢昭吴克伟韩娜高扬童赟
Owner HEFEI UNIV OF TECH