AUTOMATIC CONTENT ANALYSIS METHOD AND SYSTEM

• US 20120194681A1
• Filed: 10/07/2010
• Published: 08/02/2012
• 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
• Granted Patent: US 9,076,045 B2
• Time in Patent Office: Days
• US Class Current: 348/149
• 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:

one or more capturing devices arranged for quick mounting and further configured to capture, over time, a sequence of images of a first vehicle being located within a specified distance from a second vehicle;

a recorder configured to record, over time, momentary kinetics parameters of the second vehicle;

an analyzer configured to apply a decision function to the captured images and the momentary kinetics parameters, to yield an analysis of a risk of collision between the first vehicle and the second vehicle.

2. The system according to claim 1, further comprising a location reading module, wherein the analyzer is further configured to apply the decision function to positioning data obtained from the location reading module, such that data relevant to the risk of collision is further deduced from the positioning data.

3. The system according to claim 1, further comprising means for detecting the second vehicle'"'"'s driver condition, wherein the analyzer is further configured to apply the decision function to the detected driver condition, to yield an improved estimation of the risk of collision.

4. The system according to claim 1, further comprising means for detecting road condition, wherein the analyzer is further configured to apply the decision function to the detected road condition, to yield an improved estimation of the risk of collision.

5. The system according to claim 1, further comprising means for detecting the pedestrian on the captured images, wherein the analyzer is further configured to apply the decision function to the detected pedestrians, to yield an improved estimation of the risk of collision.

6. The system according to claim 1, wherein the system is further configured to consume electricity from a vehicle mounted power source.

7. A system comprising:

one or more mountable capturing devices configured each to capture a plurality of images each associated with specified objects in specified locations such that a specified area is covered;

a user interface configured to specify regions of interest (ROI) in each of the captured images; and

a control unit configured to;

(i) instruct the mountable capturing devices to repeat 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

compare 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.

8. A system comprising:

one or more capturing devices configured to capture over time, a sequence of images taken from a vehicle;

a recorder configured to record over time, momentary kinetics parameters of the vehicle;

a locator configured to obtain over time, global location readings of the vehicle;

an estimator configured to compute an estimated image virtually taken from the vehicle in specified time, based on the captured image taken from the vehicle and the recorded kinetics parameters and global location readings associated with the specified time;

a comparator configured to repeatedly compare captured images taken from the vehicle with computed estimated images virtually taken from vehicle to yield a comparison indicative of a deviation from a specified route of the vehicle.

9. A system comprising:

one or more capturing devices arranged for quick mounting and further configured to capture, over time, a sequence of images of a first vehicle being located within a specified distance from a second vehicle;

a recorder configured to record over time, momentary kinetics parameters of the second vehicle;

a locator configured to obtain over time, a global location reading of the second vehicle; and

an analyzer configured to apply a decision function to the captured images, the momentary kinetics parameters and the global location reading, to yield an estimated horizon line and a speed vector direction of the second vehicle.

10. A system comprising:

one or more capturing devices arranged for quick mounting and further configured to capture over time, a sequence of images taken from a plurality of devices located in a street level height of a certain area;

a locator configured to obtain over time, a global location reading of each one

of the devices; and

an analyzer configured to apply a computation function to the captured images, the momentary kinetics parameters and the global location reading, to yield a real-time map of the area.

11. The method according to claim 10, wherein the method is carried out by specifying keypoints.

12. The method according to claim 10, wherein the computation function is such that it detects for-sale advertisements in the captured images and yields a map of for-sale properties.

13. The method according to claim 10, wherein the computation function is such that it detects crime actions in the captured images and yields a map of crimes.

14. The method according to claim 10, wherein the computation function is such that it detects specified persons in the captured images and yields a map of the specified persons.

15. The method according to claim 10, wherein the computation function is such that it detects free parking spaces advertisements in the captured images and yields a map of free parking spaces.

16. A method comprising:

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 images produced in the capturing with the captured images produced in the repeating of the capturing to yield comparison between the captured objects by comparing specified ROI.

17. The method according to claim 16, wherein the comparison is usable for micro navigation.

18. The method according to claim 16, further comprising recommending driving lanes based on the comparison.

19. The method according to claim 16, further comprising generating map layers, based on the comparison.

20. The method according to claim 16, further comprising adding a layer of signs on a map containing the ROI, based on the comparison.

21. The method according to claim 16, wherein at least some stages of the method are carried out by specifying keypoints.

22. The method according to claim 16, wherein at least some stages of the method are carried out using parallel algorithms.

23. The method according to claim 16, wherein at least some stages of the method are carried out using a Graphics Processing Unit (GPU).

24. A method comprising:

capturing, over time, a sequence of images taken from a vehicle;

recording, over time, momentary kinetics parameters of the vehicle;

computing an estimated image virtually taken from the vehicle in specified time, based on the captured image taken from the vehicle and the recorded kinetics parameters and global location readings associated with the specified time;

repeatedly comparing captured images taken from the vehicle with computed estimated images virtually taken from vehicle to yield a comparison indicative of a deviation from a specified route of the vehicle.

25. The method according to claim 24, wherein the comparing is based on captured lane marks derived from the captured images.

26. A method comprising:

capturing, over time, a sequence of images of a first vehicle being located within a specified distance from a second vehicle;

recording, over time, momentary kinetics parameters of the second vehicle;

applying a decision function to the captured images, the momentary kinetics parameters, to yield an analysis of a risk of collision between the first and the second vehicles.

27. The method according to claim 26, further comprising obtaining, over time, a global location reading of the second vehicle, wherein the decision function is further applied to the global location reading of the second vehicle.

28. A method comprising:

capturing, over time, a sequence of images of a first vehicle near a second vehicle;

recording, over time, momentary kinetics parameters of the second vehicle;

obtaining, over time, a global location reading of the second vehicle;

applying a decision function to the captured images, the momentary kinetics parameters and the global location reading, to yield a speed vector direction of the second vehicle.

29. The method according to claim 28, wherein the decision function is such that the applying further yields an estimation of a horizon line associated with the captured images.

30. A method comprising:

capturing, over time, a sequence of images taken from a plurality of devices located in a street level height of a certain area;

obtaining, over time, a global location reading of each one of the devices;

applying a computation function to the captured images and the global location reading, to yield a real-time map of the area.