METHOD AND DEVICE FOR RECOGNIZING STRUCTURES IN METADATA FOR PARALLEL AUTOMATED EVALUATION OF PUBLICLY AVAILABLE DATA SETS AND REPORTING OF CONTROL INSTANCES

• US 20110029574A1
• Filed: 02/13/2009
• Published: 02/03/2011
• Est. Priority Date: 03/07/2008
• Status: Abandoned Application

• Current Assignee: Siemens AG
• Original Assignee: Siemens AG
• Inventors: Köster, Gerta, Klein, Wolfram
• Application Number:
  US12/919,155
• Publication Number:
  US 20110029574A1
• Time in Patent Office: Days
• US Class Current: 707/802
• CPC Class Codes: G06V 20/52 Surveillance or monitoring of activities, e.g. for recognising suspicious objects recognising microscopic objects G06V20/69
• CPC Class Codes: G08G 1/0104 Measuring and analyzing of parameters relative to traffic conditions

Abstract

In a method for simultaneous observation and analysis of a plurality of data sets, in particular from webcams or sensors published over the Internet, atypical situations can be detected from a plurality of data sets of mostly low quality by producing metadata that are investigated for critical structures. Moreover, atypical situations can be recognized by comparing actual object mass properties of a data set with the target object mass properties of a data set. In this way, for example, human crowds or masses in pedestrian zones, football stadiums or subway stations can be effectively monitored and the large number of freely available internet cameras can be utilized.

Claims

1. A method for simultaneously observing and analyzing a plurality of data sets comprising:

generating metadata from the data sets;

evaluating the metadata in respect of at least one of atypical situations and critical structures;

notifying a control instance, if at least one of atypical situations and critical structures are determined.

2. The method according to claim 1, further comprising

generating the metadata as a property of object masses.

3. The method according to claim 2, comprising

determining target object mass properties of data sets as a function of classes of observation locations and associated types of object masses.

4. The method according to claim 3, wherein

the categories of observation sites in a public place, stadium, stadium approach, street, of road users are at least one of partially or completely open areas and a natural environment.

5. The method according to claim 3, wherein

the types of object masses are at least one of masses of humans, vehicles, bicycles and animals.

6. The method according to claim 3, comprising

recognizing at least ore of the atypical situations and critical structures by comparing actual object mass properties of a data set with the target object mass properties.

7. The method according to claim 2, comprising

recognizing at least one at the atypical situations and critical structures in an object mass.

8. The method according to claim 3, comprising

recognizing at least one of atypical situations and critical structures by comparing actual object mass properties of an extract of a data set with the target object mass properties for this extract.

9. The method according to claim 2, comprising

recognizing at least one of situations and structures by filtering the data sets for imaging the metadata as a function of at least one of the location, the time, and by means of pattern recognition.

10. The method according to claim 3, comprising

automatically identifying at one of the classes of observation locations and the types of object masses.

11. The method according to claim 1, comprising

automatically recognizing at least one of the atypical situations and critical structures.

16. The method according to claim 1, wherein the data sets are provided by internet cameras or sensors which are publicly available by way of the Internet.

17. The method according to claim 2, wherein the property of object masses comprises at least one of:

density, distribution of density, overcrowding, flows, movement directions, speeds and/or behavior pattern of an object mass, associated minimal/maximum average values of an object mass, and past, current and future behavior of an object mass.

18. The method according to claim 7, wherein the at least one of the atypical situations and critical structures are recognized as at least one of ring formations, regular overcrowding, sharp edges, path formations and suddenly dispersing objects in an object mass.

19. The method according to claim 1, comprising

automatically recognizing at least one of the atypical situations and critical structures, and further comprising an automated notification of the control instance.

20. The method according to claim 1, comprising

an automated notification of the control instance.

12. A device for the simultaneous observation and analysis of a plurality of data sets, comprising

means for generating metadata from the data sets;

means for evaluating the metadata in respect of at least one atypical situations and critical structures;

means for notifying a control instance, if at least one of atypical situation and critical structures are determined.

13. The device according to claim 12, wherein the data sets are provided by internet cameras or sensors which are publicly available by way of the Internet.

14. The device according to claim 12, wherein the device is further operable to generate the metadata as a property of object masses comprising at least one of:

density, distribution of density, overcrowding, flows, movement directions, speeds and/or behavior pattern of an object mass, associated minimal/maximum average values of an object mass, and past, current and future behavior of an object mass.

15. The device according to claim 14, comprising

determining target object mass properties of data sets as a function of classes of observation locations and associated types of object masses.