Automatic content analysis method and system

• US 9,076,045 B2
• Filed: 10/07/2010
• Issued: 07/07/2015
• Est. Priority Date: 10/07/2009
• Status: Active Grant

• Current Assignee: iOnRoad Technologies Limited (Samsung Electronics Co. Ltd.)
• Original Assignee: Alon Atsmon, Dan Atsmon, Hilla Bass, Ram Dayan
• Inventors: Atsmon, Alon, Atsmon, Dan, Dayan, Ram, Bass, Hilla
• Primary Examiner(s): LI, TRACY Y
• Application Number:
  US13/500,409
• Publication Number:
  US 20120194681A1
• Time in Patent Office: 1,734 Days
• Field of Search: 348/149
• US Class Current: 1/1
• CPC Class Codes: B60K 28/06 responsive to incapacity of driver
• CPC Class Codes: B60W 2540/22 Psychological state; Stress level or workload
• CPC Class Codes: B60W 2540/24 Drug level, e.g. alcohol
• CPC Class Codes: B60W 2540/26 Incapacity
• CPC Class Codes: B60W 30/0953 the prediction being responsive to vehicle dynamic parameters
• CPC Class Codes: B60W 30/0956 the prediction being responsive to traffic or environmental parameters
• CPC Class Codes: B60W 40/04 Traffic conditions
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/14 Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: G01C 21/3602 Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
• CPC Class Codes: G01C 21/3815 Road data
• CPC Class Codes: G01C 21/3841 Data obtained from two or more sources, e.g. probe vehicles
• CPC Class Codes: G01C 21/3848 Data obtained from both position sensors and additional sensors
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• 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 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G08B 21/06 indicating a condition of sleep, e.g. anti-dozing alarms
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/012 from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0133 for classifying traffic situation
• CPC Class Codes: G08G 1/0141 : for traffic information dissemination
• CPC Class Codes: G08G 1/0175 : by photographing vehicles, e.g. when violating traffic rules
• CPC Class Codes: G08G 1/04 : using optical or ultrasonic detectors
• CPC Class Codes: G08G 1/0962 : having an indicator mounted inside the vehicle, e.g. giving voice messages
• CPC Class Codes: G08G 1/09623 : Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle G08G1/0967 takes precedence
• CPC Class Codes: G08G 1/13 : the indicator being in the form of a map
• CPC Class Codes: G08G 1/163 : involving continuous checking
• CPC Class Codes: G08G 1/165 : for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes
• CPC Class Codes: G08G 1/167 : Driving aids for lane monitoring, lane changing, e.g. blind spot detection
• CPC Class Codes: G08G 1/168 : Driving aids for parking, e.g. acoustic or visual feedback on parking space
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/027 : using movement velocity, acceleration information
• CPC Class Codes: H04W 4/029 : Location-based management or tracking services
• CPC Class Codes: H04W 4/40 : for vehicles, e.g. vehicle-to-pedestrians [V2P]

Abstract

A method of analyzing images over time is provided herein. The method includes: capturing a plurality of images each associated with specified objects in specified locations such that a specified area is covered; specifying regions of interest (ROI) in each of the captured images; repeating the capturing with at least one of: a different location, a different orientation, and a different timing such that the captured images are associated with the specified covered area; and comparing the captured imaged produced in the capturing with the captured imaged produced in the repeating of the capturing to yield comparison between the captured objects by comparing specified ROI.

Claims

1. A system comprising:

a server;

a plurality of smart phone applications which are executed by a plurality of smart phones, each one of said plurality of smart phones having an image capturing unit, a positioning unit, and a processing unit, wherein all of the units are physically packed within the smart phone; and

wherein while said plurality of smart phone applications are executed, each one of said plurality of smart phones are mounted in a quick mount attached to a surface within one of a plurality of vehicles;

wherein each one of said plurality of smart phones is configured to capture a sequence of images and to extract therefrom information about a plurality of surface objects located within a distance from a first vehicle from said plurality of vehicles; and

wherein each one of said plurality of smart applications instructs the respective processing unit to derive location data related to the plurality of surface objects based on positioning measurements derived from the positioning unit,

wherein each one of said plurality of smart phone applications is adapted to connect to a remote server via a wireless network and to transmit the information and the location data of the plurality of surface objects to the server, and

wherein the server is adapted to yield a report comprising a map of a city according to an analysis of the information and the location data from said plurality of smart phone applications.

2. The system according to claim 1, wherein the processing unit is further configured to apply a decision function to the captured images and to momentary kinetics parameters, to yield an analysis of a risk of a collision between the first vehicle and a second vehicle, wherein the smart phone further comprises a front camera to capture images of a driver of said first vehicle, wherein said processing unit is adapted to process said images of the driver and to detect, accordingly, physical and cognitive conditions of the driver, and wherein the processing unit is further configured to estimate said risk based on said physical and cognitive conditions.

3. The system according to claim 1, wherein the processing unit is further configured to apply a decision function to the captured images and to momentary kinetics parameters, to yield an analysis of a risk of a collision between the first vehicle and a second vehicle, and wherein the processing unit is adapted to process said sequence of images and determine, accordingly, conditions of a road, wherein the processing unit is further configured to apply the decision function to the detected conditions of the road, to yield an improved estimation of the risk of collision.

4. The system according to claim 1, wherein the processing unit is further configured to apply a decision function to the captured images and to momentary kinetics parameters, to yield an analysis of a risk of a collision between the first vehicle and a second vehicle, wherein the processing unit is adapted to process said sequence of images and detect, accordingly, pedestrians, and wherein the processing unit is further configured to apply the decision function to the detected pedestrians to yield an updated estimation of the risk of collision.

5. The system according to claim 1, wherein each one of said plurality of smart phones is further configured to consume electricity from a vehicle mounted power source.

6. The system according to claim 1, wherein said report maps vacant parking places.

7. The system according to claim 1, wherein said report maps places in a road that are candidates to be fixed.

8. The system according to claim 1, wherein said report maps businesses on a street.

9. The system according to claim 1, wherein said report maps overall status of cars in a certain region.

10. The system according to claim 1, wherein said report maps status of plants in the city.

11. The system according to claim 1, wherein said map of a city maps roads and buildings.

12. A method comprising:

providing at least one smart phone application installed in at least one smart phone having an image capturing unit, a positioning unit, and a processing unit, wherein all of the units are physically packed within the smart phone;

providing at least one quick mount unit having a first and a second end, wherein the quick mount unit is shaped to grab the at least one smart phone on the first end and further configured to attach to a surface within a vehicle of a plurality of vehicles at the second end;

capturing a sequence of images;

extracting from said sequence of images information about a plurality of surface objects located within a distance from a first vehicle from said plurality of vehicles;

deriving location data related to the plurality of surface objects based on positioning measurements derived from the positioning unit in the first vehicle;

connecting to a remote server via a wireless network; and

transmitting the information and the location data of the plurality of surface objects to the remote server;

wherein the server is adapted to yield a report comprising a map of a city according to an analysis of the information and the location data from the at least one smart phone application.

13. The method according to claim 12, wherein the at least one quick mount unit comprises a quick release cradle on the first end and a suction cup at the second end.

14. The method according to claim 12, wherein the processing unit is further configured to apply a decision function to the captured images and to momentary kinetics parameters to yield an analysis of a risk of a collision between the first vehicle and a second vehicle, wherein the processing unit is adapted to determine physical and cognitive conditions of a driver of the first vehicle, and wherein the method further comprises applying the decision function to the detected driver conditions to yield an updated estimation of the risk of collision.

15. The method according to claim 12, wherein the processing unit is further configured to apply a decision function to the captured images and to momentary kinetics parameters to yield an analysis of a risk of a collision between the first vehicle and a second vehicle, wherein the processing unit is adapted to process said sequence of images and determine, accordingly, conditions of a road, and wherein the method further comprises applying the decision function to the determined conditions of the road to yield an updated estimation of the risk of collision.

16. The method according to claim 12, wherein the processing unit is further configured to apply a decision function to the captured images and to momentary kinetics parameters to yield an analysis of a risk of a collision between the first vehicle and a second vehicle, wherein the processing unit is adapted to process said sequence of images and detect, accordingly, pedestrians on the captured images, and wherein the method further comprises applying the decision function to the detected pedestrians on the captured images to yield an updated estimation of the risk of collision.

17. The method according to claim 12, wherein the at least one smart phone is further configured to consume electricity from a vehicle-mounted power source.