Detection of animate or inanimate objects

• US 8,306,265 B2
• Filed: 03/18/2009
• Issued: 11/06/2012
• Est. Priority Date: 01/12/2009
• Status: Expired due to Fees

• Current Assignee: Eastman Kodak Company
• Original Assignee: Eastman Kodak Company
• Inventors: Fry, Peter T., Kurtz, Andrew F.
• Primary Examiner(s): Repko, Jason M
• Assistant Examiner(s): FUJITA, KATRINA R
• Application Number:
  US12/406,186
• Publication Number:
  US 20100177968A1
• Time in Patent Office: 1,329 Days
• Field of Search: 382/103, 382/224, 348/143, 348/169
• US Class Current: 382/103
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 5/05 Detecting, measuring or recording for diagnosis by means of electric currents or magnetic fields; Measuring using microwaves or radio waves impedance plethysmography A61B5/0295; measuring movement of the entire body or parts thereof A61B5/11; detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof A61B5/24
• CPC Class Codes: A61B 5/1113 Local tracking of patients, e.g. in a hospital or private home
• CPC Class Codes: A61B 5/1128 using image analysis A61B5/1127 takes precedence
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: G06V 40/20 Movements or behaviour, e.g. gesture recognition recognition of facial expressions G06V40/16
• CPC Class Codes: G08B 13/19697 Arrangements wherein non-video detectors generate an alarm themselves
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Image data representing a captured image of an environment or a portion thereof, and electric-field data representing electric fields present in the environment or a portion thereof are received. The electric-field data and the image data represent information gathered from an overlapping portion of the environment. A candidate object is identified in the overlapping portion and classified as animate or inanimate based at least upon an analysis of the image data and the electric-field data.

Claims

1. A method implemented at least in part by a data processing system, the method for detecting objects in an environment, and the method comprising:

receiving image data representing a captured image of the environment or a portion thereof;

receiving electric-field data from or derived from a bio-electric field sensing system including non-contact bio-electric field sensors, the electric-field data including bio-electric-field data sensed from bio-electric fields present in the environment or a portion thereof, and the electric-field data and the image data representing information gathered from an overlapping portion of the environment;

detecting a candidate object in the overlapping portion of the environment and classifying the candidate object as animate or inanimate, based at least upon an analysis of the image data and the electric-field data, wherein the analysis of the electric-field data includes an evaluation of whether the electric-field data includes at least a bio-electric field; and

storing information pertaining to results of the classifying in a processor-accessible memory system.

2. The method of claim 1, wherein the detecting, the classifying, or both comprise analyzing the image data using at least body shape detection, head detection, skin area detection, face detection, eye detection, motion analysis, or combinations thereof.

3. The method of claim 2, wherein the detecting, the classifying, or both comprise analyzing the image data using eye detection, and the eye detection includes the use of eye-specific deformable templates or pet-eye detection.

4. The method of claim 1, wherein if the candidate object is classified as animate, the method further comprises classifying the candidate object as human or animal.

5. The method of claim 4, wherein if the candidate object is classified as animal, the method further comprises classifying the candidate object as a particular type of animal.

6. The method of claim 1, wherein the bio-electric-field data includes heart beat activity information associated with the candidate object.

7. The method of claim 6, wherein if the candidate object is classified as animate, the method further comprises classifying the candidate object as human or animal based at least upon an analysis of the heart beat activity information.

8. The method of claim 6, wherein the heart beat activity information includes heart beat frequency, pulse shape, or timing, and the analysis of the heart beat activity information includes an analysis of the heart beat frequency, pulse shape, or timing.

9. The method of claim 1, wherein the electric-field data includes electro-encephalogram (EEG) information or electro-oculogram (EOG) information associated with the candidate object.

10. The method of claim 1, wherein the classifying includes an analysis of both the image data and the electric-field data.

11. The method of claim 1, wherein the classifying further classifies the candidate object as hidden based at least upon an analysis of the image data or the electric-field data.

12. The method of claim 1, wherein the candidate object is classified as inanimate based at least upon an analysis of the electric-field data.

13. The method of claim 1, wherein the electric-field data includes ambient electric field information representing ambient electric fields present in the environment, wherein the classifying classifies the candidate object as an inanimate moving object based at least upon an analysis of spatio-temporal changes in electric fields derived at least in part from the ambient electric field information.

14. The method of claim 1, wherein the classifying classifies the candidate object as inanimate based at least upon a detecting of differential electric fields represented in the electric-field data.

15. The method of claim 1, wherein the classifying of the candidate object includes calculation of confidence values based upon the received data from either image capture or bio-electric field sensing that indicates a probability that the candidate object correctly matches a determined classification.

16. The method of claim 1, wherein if the classifying of an object as animate or inanimate is indeterminate or indicative of an uncertain classification based upon the received data from either image capture or bio-electric field sensing, then continued detecting and classifying activities related to that object occurs using received data from at least the other of either image capture or bio-electric field sensing that was not previously used.

17. The method of claim 1, further comprising instructing a responsive device to take action based at least upon the results.

18. A method for detecting objects in a local environment comprising:

operating at least one camera to acquire images and associated image data of the local environment and objects therein;

detecting and locating candidate objects in the local environment based at least upon an analysis of the image data;

operating at least one non-contact bio-electric field sensor to acquire electric-field data including bio-electric-field data from at least one source of bio-electric fields that are present within the local environment;

detecting and locating candidate objects in the local environment based at least upon an analysis of the acquired bio-electric-field data; and

classifying at least one of the candidate objects as animate or inanimate at least by comparing the acquired bio-electric-field data and the image data.

19. The method of claim 18, wherein the bio-electric-field data is indicative of heart-beat or circulatory activity, brain activity, or eye activity.

20. The method of claim 18, wherein the analysis of the image data includes body shape detection, head detection, skin area detection, face detection, eye detection, motion analysis, or combinations thereof.

21. The method of claim 18, wherein the classifying classifies the candidate object as inanimate (a) based at least upon a detecting of differential electric fields represented in the electric-field data, or (b) detecting spatio-temporal changes in electric fields based upon an analysis of the electric-field data.

22. A processor-accessible memory system storing instructions configured to cause a data processing system to implement a method for detecting objects in an environment, wherein the instructions comprise:

instructions for receiving image data representing a captured image of the environment or a portion thereof;

instructions for receiving electric-field data from or derived from a bio-electric field sensing system using non-contact bio-electric field sensors, the electric-field data including bio-electric-field data sensed from bio-electric fields present in the environment or a portion thereof, and the electric-field data and the image data representing information gathered from an overlapping portion of the environment;

instructions for detecting a candidate object in the overlapping portion of the environment and classifying the candidate object as animate or inanimate, based at least upon an analysis of the image data and the received electric-field data, wherein the analysis of the electric-field data includes an evaluation of whether the electric-field data includes at least a bio-electric field; and

instructions for storing information pertaining to results of the classifying in a processor-accessible memory system.

23. A system comprising:

a data processing system;

a camera system communicatively connected to the data processing system;

an electric-field-sensing system that senses electric fields including bio-electric fields using non-contact bio-electric field sensors, the sensing system communicatively connected to the data processing system; and

a memory system communicatively connected to the data processing system and storing instructions configured to cause the data processing system to implement a method for identifying objects in an environment, wherein the instructions comprise;

instructions for receiving, from the camera system, image data representing a captured image of the environment or a portion thereof;

instructions for receiving, from the electric-field-sensing system, electric-field data including bio-electric-field data sensed from bio-electric fields present in the environment or a portion thereof, the electric-field data and the image data representing information gathered from an overlapping portion of the environment;

instructions for detecting a candidate object in the overlapping portion of the environment and classifying the candidate object as animate or inanimate based at least upon an analysis of the image data and the electric-field data, wherein the analysis of the electric-field data includes an evaluation of whether the electric-field data includes at least a bio-electric field; and

instructions for storing information pertaining to results of the classifying in the processor-accessible memory system.

24. The system of claim 23, wherein the non-contact bio-electric field sensors are electrical potential probes that measure displacement currents caused by the bio-electric fields.

25. The system of claim 23, further comprising an electric field emission system communicatively connected to the data processing system, the electric field emission system including one or more electric field emitters that emit known electric fields into the environment, the emitted electric fields interacting with objects in the environment and providing return electrical fields sensed by the electric-field-sensing system.

26. The system of claim 23, further comprising secondary sensors communicatively connected to the data processing system, wherein the instructions further comprise instructions for receiving secondary sensing data from the secondary sensors, the secondary sensor data representing audio information, microwave information, ultrasound information, other electric field information, or combinations thereof, present in the environment, and wherein the instructions for detecting and classifying include instructions for analyzing the secondary sensing data.

27. The system of claim 23, further comprising a responsive device communicatively connected to the data processing system, wherein the instructions further comprise instructions for causing the responsive device to take action based at least upon the results of the classifying.