# US 2020/0401822 A1 · Monocular cued detection of three-dimensional structures from depth images > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2020/0401822 A1 - Document ID: US20200401822A1 - Status: Published application - Kind: A1 - Application number: 17/011,798 - Filed: 2020-09-03 - Published: 2020-12-24 - Earliest claimed: 2010-09-21 - Assignees: Mobileye Vision Technologies Ltd. - Applicants: Mobileye Vision Technologies Ltd. - Inventors: Oded BERBERIAN; Gideon STEIN - Web page: https://patentgrep.com/patent/US20200401822A1 ## CPC - 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 - G06V 20/588: {Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road} - H04N 13/271: wherein the generated image signals comprise depth maps or disparity maps - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## 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 ### Claim at position 1 1-10. (canceled) ### Claim 11 A method for detection of obstacles, the method performed by a system mountable in a vehicle, wherein the system includes a processor operatively connectible to a camera, the method comprising: capturing a plurality of two-dimensional image frames in a field of view of the camera; detecting, in the image frames, a two-dimensional line associated with a candidate three-dimensional object in an environment of the vehicle using monocular information; portioning the image frames around the line to produce low-resolution image portions including the line and high-resolution image portions not including the line; computing depth values for points around the line in the low-resolution image portions, including depth values related to pixels on the line; and identifying, using a depth map, the object associated with the line. ### Claim 12 (depends on 11) The method of claim 11, further comprising: representing the object with a plurality of models; and computing a plurality of depth values based on the respective models. ### Claim 13 (depends on 12) The method of claim 12, further comprising: comparing the depth values for points around the line with model depth maps; and wherein identifying the object represented by the line is further based on the comparison. ### Claim 14 (depends on 12) The method of claim 12, wherein the models are selected from a 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. ### Claim 15 (depends on 11) The method of claim 11, wherein computing the depth values is performed around the line in the image portions. ### Claim 16 (depends on 11) The method of claim 11, further comprising: adjusting a resolution of the computation of the depth values to achieve an accuracy determined based on the line. ### Claim 17 A system for detection of obstacles, the system mountable in a host vehicle, the system including a processor operatively connectible to a camera, wherein the processor is configured to: capture a plurality of two-dimensional image frames in a field of view of the camera; detect, in the image frames, a two-dimensional line associated with a candidate three-dimensional object in an environment of the vehicle using monocular information; portion the image frames around the line to produce low-resolution image portions including the line and high-resolution image portions not including the line; compute depth values for points around the line in the low-resolution image portions, including depth values related to pixels on the line; and identify, using a depth map, the object associated with the line. ### Claim 18 (depends on 17) The system of claim 17, wherein the processor is further configured to: represent the object with a plurality of models; and compute a plurality of depth values based on the respective models. ### Claim 19 (depends on 18) The system of claim 18, wherein the processor is further configured to: compare the depth values for points around the line with the model depth maps; and wherein identifying the object represented by the line is further based on the comparison. ### Claim 20 (depends on 18) The system of claim 18, wherein the models are selected from a 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. ### Claim 21 (depends on 17) The system of claim 17, wherein the depth values are computed around the line in the image portions. ### Claim 22 (depends on 17) The system of claim 17, wherein the processor is further configured to: adjust a resolution of the computation of the depth values to achieve an accuracy determined based on the line. ### Claim 23 (depends on 17) The system of claim 17, wherein the processor is further configured to: identify a horizon in the image frames, and wherein a two-dimensional candidate is detected in the image frames below the horizon. ### Claim 24 A non-transitory computer-readable storage medium storing instructions for detection of obstacles, the instructions causing one or more processors to: capture a plurality of two-dimensional image frames in a field of view of the camera; detect, in the image frames, a two-dimensional line associated with a candidate three-dimensional object in an environment of the vehicle using monocular information; portion the image frames around the line to produce low-resolution image portions including the line and high-resolution image portions not including the line; compute depth values for points around the line in the low-resolution image portions, including depth values related to pixels on the line; and identify, using a depth map, the object associated with the line. ### Claim 25 (depends on 24) The non-transitory computer-readable storage medium of claim 24, wherein the one or more processors are configured to: represent the object with a plurality of models; and compute a plurality of depth values based on the respective models. ### Claim 26 (depends on 25) The non-transitory computer-readable storage medium of claim 25, wherein the one or more processors are configured to: compare the depth values for points around the line with the model depth maps; and wherein identifying the object represented by the line is further based on the comparison. ### Claim 27 (depends on 25) The non-transitory computer-readable storage medium of claim 25, wherein the models are selected from a 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. ### Claim 28 (depends on 24) The non-transitory computer-readable storage medium of claim 24, wherein the depth values are computed around the line in the image portions. ### Claim 29 (depends on 24) The non-transitory computer-readable storage medium of claim 24, wherein the one or more processors are configured to: adjust a resolution of the computation of the depth values to achieve an accuracy determined based on the line. ### Claim 30 (depends on 24) The system of claim 24, wherein the processor is further configured to: identify a horizon in the image frames, and wherein a two-dimensional candidate is detected in the image frames below the horizon.