MONOCULAR CUED DETECTION OF THREE-DIMENSIONAL STRUCTURES FROM DEPTH IMAGES

• US 20140160244A1
• Filed: 02/14/2014
• Published: 06/12/2014
• Est. Priority Date: 09/21/2010
• Status: Active Grant

• Current Assignee: Mobileye Technologies Limited (Intel Corporation)
• Original Assignee: Mobileye Technologies Limited (Intel Corporation)
• Inventors: Stein, Gideon, Berberian, Oded
• Granted Patent: US 10,776,635 B2
• Time in Patent Office: Days
• US Class Current: 348/46
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: H04N 13/271 wherein the generated image signals comprise depth maps or disparity maps

Abstract

Detection of three dimensional obstacles using a system mountable in a host vehicle including a camera connectible to a processor. Multiple image frames are captured in the field of view of the camera. In the image frames, an imaged feature is detected of an object in the environment of the vehicle. The image frames are portioned locally around the imaged feature to produce imaged portions of the image frames including the imaged feature. The image frames are processed to compute a depth map locally around the detected imaged feature in the image portions. Responsive to the depth map, it is determined if the object is an obstacle to the motion of the vehicle.

Claims

1. A method for detection of three dimensional obstacles, the method performed by a system mountable in a host vehicle, wherein the system includes a camera operatively connectible to a processor, the method comprising:

capturing a plurality of image frames in the field of view of the camera; and

in the image frames, detecting an imaged feature of an object in the environment of the vehicle;

portioning the image frames locally around the imaged feature to produce image portions of the image frames including the imaged feature;

processing the image frames thereby computing a depth map locally around the detected imaged feature in said image portions, wherein the depth map includes an image of the feature with a color or grayscale coordinate related to a function of distance from the camera to the object; and

responsive to the depth map, determining if the object is an obstacle to the motion of the vehicle.

2. The method of claim 1, further comprising:

representing the object with a plurality of models,

computing a plurality of model depth maps of the respective models;

comparing the depth map of the detected feature with the model depth maps; and

based on the comparison, said determining that the object is an obstacle or not an obstacle to the motion of the vehicle.

3. The method of claim 2, wherein the models are selected from the group consisting of:

a horizontal planar model, a vertical planar model, a mixed model including horizontal and vertical portions, a spherical model, a circular model, a model of a guard rail, a model of lane marker, a model of a road curb and a model of an upright pedestrian.

4. The method of claim 1, wherein said computing the depth map is performed only locally around the detected feature in said image portions.

5. The method of claim 1, further comprising:

adjusting the resolution of the computation of the depth map only to achieve an accuracy required based on the imaged feature.

6. A system for detection of three dimensional obstacles, the system mountable in a host vehicle, the system including a camera operatively connectible to a processor, the system operable to:

capture a plurality of image frames in the field of view of the camera;

detect an imaged feature in the image frames of an object in the environment of the vehicle;

portion the image frames locally around the imaged feature to produce imaged portions of the image frames including the imaged feature;

process the image frames and compute thereby a depth map locally around the detected imaged feature in said image portions, wherein the depth map includes an image of the feature with a color or grayscale coordinate related to a function of distance from the camera to the object; and

responsive to the depth map, determine if the object is an obstacle to the motion of the vehicle.

7. The system of claim 6, further operable to:

represent the object with a plurality of models,

compute a plurality of model depth maps of the respective models;

compare the depth map of the detected feature with the model depth maps; and

based on the comparison, determine that the object is an obstacle or not an obstacle to the motion of the vehicle.

8. The system of claim 7, wherein the models are selected from the group consisting of:

a horizontal planar model, a vertical planar model, a mixed model including horizontal and vertical portions, a spherical model, a circular model, a model of a guard rail, a model of lane marker, a model of a road curb and a model of an upright pedestrian.

9. The system of claim 6, wherein the depth map is computed only locally around the detected feature in said image portions.

10. The system of claim 6, further operable to:

adjust the resolution of the computation of the depth map only to achieve an accuracy required based on the imaged feature.