Videotracking

• US 8,542,874 B2
• Filed: 12/18/2007
• Issued: 09/24/2013
• Est. Priority Date: 07/11/2007
• Status: Expired due to Fees

• Current Assignee: Deltatre AG (Deltatre SpA)
• Original Assignee: Cairos Technologies Ag
• Inventors: Hoeflinger, Florian
• Primary Examiner(s): Patel, Jayesh A
• Assistant Examiner(s): Kholdebarin, Iman K
• Application Number:
  US12/668,650
• Publication Number:
  US 20100278386A1
• Time in Patent Office: 2,107 Days
• Field of Search: 382/103, 348/169
• US Class Current: 382/103
• CPC Class Codes: G06T 2207/30221 Sports video; Sports image
• CPC Class Codes: G06T 7/292 Multi-camera tracking
• CPC Class Codes: G06V 20/40 in video content extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44

Abstract

A method for tracking an object in a sequence of video frames includes the following steps: creating a model with characteristic features for the object to be tracked; and performing a template matching algorithm in individual frames on the basis of the created model for determining a position of the object in the respective frame. An apparatus arrangement for performing the method includes at least one video camera (10, 12), at least one monitor (24, 26), one computer (20) and one input device (28) for an observer (22).

Claims

1. A method for tracking an object in a sequence of video frames, characterized by the following steps:

before performing a template matching algorithm;

creating a model with characteristic features for the object to be tracked;

wherein a first component of the model contains at least one geometric dimension being a geometric shape of the object; and

wherein a second component of the model contains a color of the object;

defining a region of interest, ROI, inside which the object should be tracked; and

performing the template matching algorithm in individual frames of the sequence of video frames on the basis of the created model for determining a position of the object in the respective frame, the template matching algorithm consisting of moving the template over a search region of the respective frame, wherein the search region is determined by a dynamic prediction for the object and comparing the template with the search region pixel by pixel, the comparing using both the first component and the second component;

characterized in that;

in dependence on the result of the template matching algorithm, an uncertainty factor is assigned to the position of the object determined by the template matching algorithm;

a correction algorithm is included in the determination of the position of the object, when the uncertainty factor exceeds an uncertainty threshold; and

as a first measure, the correction algorithm continues to automatically output a position forecast by the prediction as the current object position over a certain period, until the uncertainty factor falls below the uncertainty threshold.

2. The method according to claim 1, characterized in that the first component is determined on the basis of a background subtraction.

3. The method according to claim 1, characterized in that the first component is determined on the basis of a calibration with respect to another object with known position and known dimension.

4. The method according to claim 1, characterized in that the first component is determined on the basis of a manual annotation by an observer.

5. The method according to any of claims 2 to 4, characterized in that the first component is provided with a position-dependent correction function.

6. The method according to claim 1, characterized in that the second component is a color dependent on the first component.

7. The method according to any of claims 1 or 2 to 4, characterized in that when creating the model, object regions are defined, to which certain colors are allocated.

8. The method according to claim 7, characterized in that when performing the template matching algorithm, a weighting of the object regions is included.

9. The method according to claim 7, characterized in that before performing the template matching algorithm, wherein the ROI is further defined such that other objects are tracked inside thereof and further regions are defined inside thereof.

10. The method according to claim 1, characterized in that when determining the search region, the positions of the object in a specific number of previous frames are considered.

11. The method according to claim 1, characterized in that when determining the search region, a maximum reach of the object relative to its last position is determined.

12. The method according to claim 1, characterized in that as a second measure, the correction algorithm continues to output a position, which was determined as an object position before exceeding the uncertainty threshold, as the current object position, until the uncertainty factor falls below the uncertainty threshold again.

13. The method according to claim 1, characterized in that an observer manually intervenes to correct a false allocation of positions of said object.

14. The method according to claim 13, characterized in that an overlap of two objects is detected and analyzed automatically by a merging algorithm, whereupon the observer is given the opportunity to correct the result of the merging algorithm.

16. The method according to claim 1, characterized in that the template matching algorithm defines colors according to a HSV color model.

17. The method according to claim 1, characterized in that each template matching algorithm is performed on a real-time basis and the positions of the object determined are stored on a real-time basis.

18. The method according to claim 1, characterized in that successively determined position data of the object are smoothed.

19. The method according to claim 1, characterized in that the determined position data of the object are transformed into coordinates of a three-dimensional reference system, preferably into world coordinates.

20. The method according to claim 1, characterized in that in the sequence of video frames a plurality of objects are tracked at the same time.

21. An apparatus arrangement for performing the method according to any one of claims 1, 6, 10, 11, or 2-4 or 12-20 inclusive, comprising at least one video camera, at least one monitor, one computer and one input device for an observer.

22. The apparatus arrangement according to claim 21, characterized in that two video cameras are provided, which are arranged such that they detect two different regions of a ROI within which the object is to be tracked.

23. The method according to claim 1, characterized in that after a manual intervention by an observer, the correction algorithm checks when the uncertainty factor has exceeded the uncertainty threshold and automatically corrects a false allocation also for the period between the exceeding of the uncertainty threshold and the manual intervention.

15. A method for tracking an object in a sequence of video frames, characterized by the following steps:

before performing a template matching algorithm;

creating a model with characteristic features for the object to be tracked;

wherein a first component of the model contains at least one geometric dimension being a geometric shape of the object; and

wherein a second component of the model contains a color of the object;

defining a region of interest, ROI, inside which the object should be tracked; and

performing the template matching algorithm in individual frames of the sequence of video frames on the basis of the created model for determining a position of the object in the respective frame, the template matching algorithm consisting of moving the template over a search region of the respective frame, wherein the search region is determined by a dynamic prediction for the object and comparing the template with the search region pixel by pixel, the comparing using both the first component and the second component;

characterized in that;

in dependence on the result of the template matching algorithm, an uncertainty factor is assigned to the position of the object determined by the template matching algorithm; and

a correction algorithm is included in the determination of the position of the object, when the uncertainty factor exceeds an uncertainty threshold; and

wherein after a manual intervention by an observer, the correction algorithm checks when the uncertainty factor has exceeded the uncertainty threshold and automatically corrects a false allocation also for the period between the exceeding of the uncertainty threshold and the manual intervention.