AUTOMATED RUNNING-ENGINE DETECTION IN STATIONARY MOTOR VEHICLES

• US 20130201324A1
• Filed: 02/02/2012
• Published: 08/08/2013
• Est. Priority Date: 02/02/2012
• Status: Active Grant

• Current Assignee: Conduent Business Services, LLC (Conduent, Inc.)
• Original Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Inventors: Cardoso, George Cunha, Bernal, Edgar A., Pennington, Graham
• Granted Patent: US 9,569,680 B2
• Time in Patent Office: Days
• US Class Current: 348/135
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 2207/10024 Color image
• CPC Class Codes: G06T 2207/10048 Infrared image
• CPC Class Codes: G06T 2207/10141 Special mode during image acquisition
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30232 Surveillance
• CPC Class Codes: G06T 7/40 Analysis of texture depth or shape recovery from texture G06T7/529
• CPC Class Codes: G06T 7/44 using image operators, e.g. filters, edge density metrics or local histograms
• CPC Class Codes: G06V 10/145 Illumination specially adapted for pattern recognition, e.g. using gratings

Abstract

A method for idling vehicle detection comprises shining a light source on an exterior surface of a vehicle with an illumination module, collecting light reflected from the exterior surface of the vehicle with a capture module, and processing the collected light reflected from the exterior surface of the vehicle surface with a processing module wherein the processing model determines if the vehicle'"'"'s engine is running or if the vehicle'"'"'s engine is not running.

Claims

1. A method for idling vehicle detection, said method comprising:

shining a light source on a surface of a vehicle with an illumination module;

collecting light reflected from said surface of said vehicle with a capture module; and

processing said collected light reflected from said surface of said vehicle surface with a processing module wherein said processing module determines if said vehicle'"'"'s engine is running or if said vehicle'"'"'s engine is not running.

2. The method of claim 1 wherein said capture module comprises at least one of a still camera and a video camera configured to possess a circle of confusion greater than 1 pixel thereby allowing light reflected from said surface of said vehicle to form an interference pattern on a sensor of at least one of said still camera and said video camera.

3. The method of claim 2 wherein processing said collected light reflected from said surface of said vehicle further comprises:

analyzing a texture appearance of said interference pattern.

4. The method of claim 3 wherein said texture appearance of said interference pattern is determined by one of:

a local binary pattern analysis;

a scale-invariant feature transformation;

a process of Gabor filtering; and

a texton-based approach.

5. The method of claim 3 wherein said processing module is further configured for:

identifying a speckle associated with said interference pattern;

assigning a relative texture label to each pixel associated with said identified speckle; and

comparing a texture appearance metric comprising said relative texture labels from said identified speckle to at least one texture appearance metric from a speckle associated with a running engine and at least one texture appearance metric from a speckle associated with a non-running engine to determine if said texture appearance metric resulted from a vehicle with an engine running or from a vehicle with an engine not running.

6. The method of claim 5, wherein said comparison is conducted using one of:

a nearest neighbor classifier;

a support vector machine classifier;

a Bayes classifier; and

a neural network classifier.

7. The method of claim 1 wherein said light source comprises a coherent, low-power light source, and wherein said coherent low-power light source is at least one of:

a visible light source;

a near infrared light source; and

an infrared light source.

8. The method of claim 1, wherein said method further comprises:

monitoring a plurality of vehicles distributed in an environment to determine if any of said plurality of vehicles are idling.

9. A system for detecting idling vehicles comprising:

an illumination module configured to shine a light source on a surface of a vehicle;

a capture module configured to collect light reflected from said surface of said vehicle, said light being propagated by said illumination module; and

a processing module for processing said collected light reflected from said surface of said vehicle, wherein said processing model determines if said vehicle'"'"'s engine is running or if said vehicle'"'"'s engine is not running.

10. The system of claim 9 wherein said capture module comprises at least one of a still camera and a video camera configured to possess a circle of confusion of more than 1 pixel, thereby allowing light reflected from said surface of said vehicle to form an interference pattern on a sensor of at least one of said still camera and said video camera.

11. The system of claim 10 wherein said processing module processes said collected light reflected from said surface of said vehicle by analyzing a texture appearance of said interference pattern.

12. The system of claim 10 wherein said processing module determines a texture appearance of said interference pattern by one of:

a local binary pattern analysis;

a scale-invariant feature transformation;

a process of Gabor filtering; and

a texton-based approach.

13. The system of claim 11 wherein said processing module is further configured for:

identifying a speckle associated with said interference pattern;

assigning a relative texture label to each pixel associated with said identified speckle; and

comparing a texture appearance metric comprising said relative texture labels from said identified speckle to at least one texture appearance metric from a speckle associated with a running engine and at least one texture appearance metric from a speckle associated with a non-running engine to determine if said texture appearance metric resulted from a vehicle with an engine running or from a vehicle with an engine not running.

14. The system of claim 13, wherein said processing module compares said texture appearance metrics according to one of:

a nearest neighbor classifier;

a support vector machine classifier;

a Bayes classifier; and

a neural network classifier.

15. The system of claim 9 wherein said light source comprises a coherent, low-power light source, and wherein said coherent low-power light source is at least one of:

a visible light source;

a near infrared light source; and

an infrared light source.

16. The system of claim 9, wherein said system is deployed in an environment and wherein said system monitors a plurality of vehicles distributed in said environment to determine if any of said plurality of vehicles is idling.

17. A method for idling vehicle detection, said method comprising:

shining a light source on a surface of a vehicle, wherein said light source comprises a coherent, low-power light source;

collecting light reflected from said surface of said vehicle with a capture module, wherein said capture module is configured to possess a circle of confusion greater than 1 pixel thereby allowing light reflected from said surface of said vehicle to form an interference pattern on a sensor of said capture module; and

processing said collected light reflected from said surface of said vehicle surface with a processing module wherein processing said collected light reflected from said surface of said vehicle further comprises analyzing a texture appearance of said interference pattern to determine if said vehicle'"'"'s engine is running or if said vehicle'"'"'s engine is not running.

18. The method of claim 17 wherein said coherent low-power light source is at least one of:

a visible light source;

a near infrared light source; and

an infrared light source.

19. The method of claim 18 wherein said texture appearance of said interference pattern is determined by one of:

a local binary pattern analysis;

a scale-invariant feature transformation;

a process of Gabor filtering; and

a texton-based approach.

20. The method of claim 19, further comprising:

monitoring a plurality of vehicles distributed in an environment to determine if any of said plurality of vehicles are idling.