IMAGE-PROCESSING DEVICE, SURVEILLANCE SYSTEM, METHOD FOR ESTABLISHING A SCENE REFERENCE IMAGE, AND COMPUTER PROGRAM

• US 20080019566A1
• Filed: 07/18/2007
• Published: 01/24/2008
• Est. Priority Date: 07/21/2006
• Status: Active Grant

• Current Assignee: Robert Bosch GmbH
• Original Assignee: Robert Bosch GmbH
• Inventors: Mueller-Scheiders, Stefan, Merkel, Marcel, Niem, Wolfgang
• Granted Patent: US 8,285,044 B2
• Time in Patent Office: Days
• US Class Current: 382/103
• 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: G06V 20/54 of traffic, e.g. cars on the road, trains or boats

Abstract

Video surveillance systems are used in a multitude of applications in order to monitor large areas. Such video surveillance systems customarily include a plurality of fixed-mounted cameras that record relevant surveillance scenes and a central surveillance station in which the video sequences of the cameras are brought together. Often, the evaluation of the assembled video data is performed by surveillance personnel. As is known, the surveillance activities of surveillance personnel are very fatiguing, which is why possibility that important events in the surveillance scenes may be overlooked cannot be ruled out. For this reason, it has long been common practice to use known image-processing algorithms for automated evaluation of video sequences. An image-processing device is proposed for carrying out an object segmentation in which the object segmentation is executed and/or is executable through comparison of a camera image to a scene reference image of a surveillance scene, equipped with a learning device for generating the scene reference image; the learning device is embodied to generate the scene reference image through evaluation of a medium-term and/or long-term observation of the surveillance scene

• a) that extends over a time period of longer than one day, preferably longer than several days, in particular longer than 1 week, and/or
• b) that extends over a time period that includes several states of the surveillance scene.

Claims

1. An image-processing device for carrying out an object segmentation, comprising means for executing the object segmentation through comparison of a camera image to a received reference image of a surveillance scene;

a learning device for generating the scene reference image;

said learning device being configured to generate the scene reference image through evaluation of a medium-term and/or long-term observation of the surveillance scene that occurs in the situation selected from the group consisting of occurring over a time period of longer than one day, occurring over a time period which includes several states of the reference scenes, and both.

2. An image-processing device as defined in claim 1, wherein said learning device is configured so as to generate the scene reference image through the evaluation of a medium-term or long-term observation of the surveillance scene that occurs longer than several days.

3. An image-processing device as defined in claim 1, wherein said learning device is configured so as to generate the scene reference image through the evaluation of a medium-term or long-term observation of the surveillance scene that occurs longer than one week.

4. An image-processing device as defined in claim 1, wherein said learning device is configured to generate a scene model of the surveillance scene.

5. An image-processing device as defined in claim 5, wherein said learning device is configured so that it generates the scene model including objects selected from the group consisting of trajectory-based objects, image-based objects, and both.

6. An image-processing device as defined in claim 5, wherein said learning device is configured so as to generate the scene model including the trajectory-based objects selected from the group consisting of paths, walkways, streets, and entrances, exits, static objects, and combinations thereof.

7. An image-processing device as defined in claim 5, wherein said learning device is configured so as to generate the scene model including the image-based objects selected from the group consisting of interference regions, reflective regions, shaded regions, and combinations thereof.

8. An image-processing device as defined in claim 6 wherein said learning device is configured to generate the scene model in form of a multi-mode scene model and individual modes of said multi-mode scene model are distinguished through different states of the surveillance scene.

9. An image-processing device as defined in claim 8, wherein the states of the surveillance scene differ with regard to one or more of parameters selected from the group consisting of hour, time of day, date, relative position of a surveillance camera, relative orientation of a surveillance camera, absolute position of a surveillance camera, absolute orientation of the surveillance camera, weather conditions, lighting conditions, day of the week, season, and lunar phase.

10. A surveillance system, comprising an image processing device for carrying out an object segmentation and including means for executing the object segmentation through comparison of a camera image to a received reference image of a surveillance scene, a learning device for generating the scene reference image, said learning device being configured to generate the scene reference image through evaluation of a medium-term and/or long-term observation of the surveillance scene that occurs in the situation selected from the group consisting of occurring over a time period of longer than one day, occurring over a time period which includes several states of the reference scenes, and both;

a plurality of interfaces for connection to surveillance cameras; and

an evaluation device that is configured to establish proximity relationship between said surveillance cameras through use of trajectory-based objects.

11. The surveillance system as defined in claim 11, wherein the trajectory-based objects are implemented in form selected from the group consisting of entrances, exits, and both.

12. A method for generating a scene reference image of a surveillance scene for an object segmentation in image processing, comprising the steps of generating a scene reference through an evaluation of a medium-term and/or long-term observation of the surveillance scene that occurs over a time period selected from the group consisting of a time period of longer than one day, over a time period which includes several states of the surveillance scene, and both.

13. A method as defined in claim 12, wherein said generating of the scene reference image includes generating the scene reference image over a time period longer than several days.

14. A method as defined in claim 12, wherein said generating of the scene reference image includes generating the scene reference image over a time period longer than one week.

15. A computer program with programming code means for executing a method an image processing device for carrying out an object segmentation and including means for executing the object segmentation through comparison of a camera image to a received reference image of a surveillance scene, a learning device for generating the scene reference image, said learning device being configured to generate the scene reference image through evaluation of a medium-term and/or long-term observation of the surveillance scene that occurs in the situation selected from the group consisting of occurring over a time period of longer than one day, occurring over a time period which includes several states of the reference scenes, and both;

a plurality of interfaces for connection to surveillance cameras; and

an evaluation device that is configured to establish proximity relationship between said surveillance cameras through use of trajectory-based objects, means for executing the object segmentation through comparison of a camera image to a received reference image of a surveillance;

a learning device for generating the scene reference image;

said learning device being configured to generate the scene reference image through evaluation of a medium-term and/or long-term observation of the surveillance scene that occurs in the situation selected from the group consisting of occurring over a time period of longer than one day, occurring over a time period which includes several states of the reference scenes, and both, when the program is executed on a computer