# US 10,769,799 B2 · Foreground detection > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,769,799 B2 - Document ID: US10769799B2 - Status: Granted - Kind: B2 - Application number: 16/111,589 - Filed: 2018-08-24 - Published: 2020-02-27 - Granted: 2020-09-08 - Earliest claimed: 2018-08-24 - Assignees: Ford Global Technologies, LLC - Applicants: Ford Global Technologies, LLC - Inventors: Bruno Sielly Jales Costa; Enrique Corona; Gintaras Vincent Puskorius; Dimitar Petrov Filev - Web page: https://patentgrep.com/patent/US10769799B2 ## CPC - G06T 7/254: involving subtraction of images - B60W 10/04: including control of propulsion units - B60W 10/18: including control of braking systems - B60W 10/20: including control of steering systems - B60W 30/09: Taking automatic action to avoid collision, e.g. braking and steering - G06T 7/248: {involving reference images or patches} - G06V 10/435: {Computation of moments} - G06V 20/54: of traffic, e.g. cars on the road, trains or boats - G06V 20/56: exterior to a vehicle by using sensors mounted on the vehicle - G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]} - G08G 1/0116: {from roadside infrastructure, e.g. beacons} - G08G 1/0129: {for creating historical data or processing based on historical data} - G08G 1/0141: {for traffic information dissemination} - G08G 1/04: using optical or ultrasonic detectors - G08G 1/163: {involving continuous checking} - G08G 1/166: {for active traffic, e.g. moving vehicles, pedestrians, bikes} - B60W 2420/403: Image sensing, e.g. optical camera - B60W 2554/00: Input parameters relating to objects - G06T 2207/10016: Video; Image sequence - G06T 2207/10024: Color image - G06T 2207/30236: Traffic on road, railway or crossing - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) ## Abstract A computing system can receive an image including foreground pixels. The foreground pixels can be determined based on determining eccentricity εk based on a sequence of images acquired by a stationary sensor. The vehicle can determine moving objects in the image based on the foreground pixels. The vehicle can be operated based on the moving objects in the image. ## Claims ### Claim 1 A method, comprising: receiving an image including foreground pixels, the foreground pixels determined based on determining an eccentricity εk that is a measure of a difference of intensities of respective pixels from past intensity values, for a sequence of images acquired by a stationary sensor; determining moving objects in the image based on the foreground pixels; and operating a vehicle based on the moving objects in the image. ### Claim 2 (depends on 1) The method of claim 1, further comprising determining eccentricity εk based on determining a mean μk for pixels of the sequence of images, based on a previous mean μk-1 according to equation μk=(1−α)μk-1+αxk where α is an empirically determined constant. ### Claim 3 (depends on 2) The method of claim 2, further comprising determining the eccentricity εk based on determining a variance σk2 for pixels of the sequence of images, based on a previous variance σk-12 and the mean μk according to equation σ k 2 = ( 1 - α ) ⁢ σ k - 1 2 + α ⁡ ( x k - μ k ) T ⁢ ( x k - μ k ) 1 - α . ### Claim 4 (depends on 3) The method of claim 3, further comprising determining the eccentricity εk based on the mean μk and the variance σk2 according to equation ɛ k = α + α ⁡ ( x k - μ k ) T ⁢ ( x k - μ k ) σ k 2 . ### Claim 5 (depends on 4) The method of claim 4, further comprising determining the foreground pixels by comparing εk to α times an empirically determined constant. ### Claim 6 (depends on 1) The method of claim 1, further comprising determining moving objects in the image based on determining connected regions of foreground pixels with empirically determined minimum and maximum areas. ### Claim 7 (depends on 6) The method of claim 6, wherein the connected regions of foreground pixels are determined based on having similar eccentricity εk. ### Claim 8 (depends on 1) The method of claim 1, wherein the stationary sensor is a red, green blue (RGB) color video camera included in a traffic infrastructure system. ### Claim 9 (depends on 8) The method of claim 8, wherein the vehicle receives the image including foreground pixels from the traffic infrastructure system via a network based on a location of the vehicle. ### Claim 10 A system, comprising a processor; and a memory, the memory including instructions to be executed by the processor to: receive an image including foreground pixels, the foreground pixels determined based on determining an eccentricity εk, that is a measure of a difference of intensities of respective pixels from past intensity values, for a sequence of images acquired by a stationary sensor; determining moving objects in the image based on the foreground pixels; and operating a vehicle based on the moving objects in the image. ### Claim 11 (depends on 10) The system of claim 10, further comprising determining the eccentricity εk based on determining a mean μk for pixels of the sequence of images, based on a previous mean μk-1 according to equation μk=(1−α)μk-1+αxk. ### Claim 12 (depends on 11) The system of claim 11, further comprising determining the eccentricity εk based on determining a variance based on σk2 for pixels of the sequence of images, based on a previous variance σk-12 and the mean μk according to equation σ k 2 = ( 1 - α ) ⁢ σ k - 1 2 + α ⁡ ( x k - μ k ) T ⁢ ( x k - μ k ) 1 - α . ### Claim 13 (depends on 12) The system of claim 12, further comprising determining the eccentricity εk based on the mean μk and the variance σk2 according to equation ɛ k = α + α ⁡ ( x k - μ k ) T ⁢ ( x k - μ k ) σ k 2 . ### Claim 14 (depends on 13) The system of claim 13, further comprising determining the foreground pixels by comparing εk to α times an empirically determined constant. ### Claim 15 (depends on 10) The system of claim 10, further comprising determining moving objects in the image based on determining connected regions of foreground pixels with empirically determined minimum and maximum areas. ### Claim 16 (depends on 15) The system of claim 15, wherein the connected regions of foreground pixels are determined based on having similar eccentricity εk. ### Claim 17 (depends on 10) The system of claim 10, wherein the stationary sensor is a red, green blue (RGB) color video camera included in a traffic infrastructure system. ### Claim 18 (depends on 17) The system of claim 17, wherein the vehicle receives the image including foreground pixels from the traffic infrastructure system via a network based on a location of the vehicle. ### Claim 19 A system, comprising: means for controlling vehicle steering, braking and powertrain; computer means for: receiving an image including foreground pixels the foreground pixels determined based on determining an eccentricity εk that is a measure of a difference of intensities of respective pixels from past intensity values, for a sequence of images acquired by a stationary sensor; determining moving objects in the image based on the foreground pixels; and operating a vehicle based on the moving objects in the image and based on the means for controlling vehicle steering, braking and powertrain. ### Claim 20 (depends on 19) The system of claim 19, further comprising determining the eccentricity εk based on determining a mean μk based on equation μk=(1−α)μk-1+αxk.