METHOD AND APPARATUS FOR TRACKING OBJECT, AND METHOD AND APPARATUS FOR CALCULATING OBJECT POSE INFORMATION

• US 20150131859A1
• Filed: 12/12/2014
• Published: 05/14/2015
• Est. Priority Date: 02/07/2007
• Status: Active Grant

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: KIM, Jung-bae, WANG, Haitao
• Granted Patent: US 10,049,277 B2
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06T 3/02 Affine transformations for image registration G06T3/147; for image mosaicing G06T3/4038
• CPC Class Codes: G06T 3/147 using affine transformations
• CPC Class Codes: G06T 7/20 Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/246 using feature-based methods, e.g. the tracking of corners or segments
• CPC Class Codes: G06T 7/248 involving reference images or patches
• CPC Class Codes: G06T 7/38 Registration of image sequences
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: G06V 10/62 relating to a temporal dimension, e.g. time-based feature extraction; Pattern 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
• CPC Class Codes: G06V 40/161 Detection; Localisation; Normalisation
• CPC Class Codes: G06V 40/167 using comparisons between temporally consecutive images
• CPC Class Codes: G06V 40/168 Feature extraction; Face representation

Abstract

A method and apparatus for tracking an object, and a method and apparatus for calculating object pose information are provided. The method of tracking the object obtains object feature point candidates by using a difference between pixel values of neighboring frames. A template matching process is performed in a predetermined region having the object feature point candidates as the center. Accordingly, it is possible to reduce a processing time needed for the template matching process. The method of tracking the object is robust in terms of sudden changes in lighting and partial occlusion. In addition, it is possible to track the object in real time. In addition, since the pose of the object, the pattern of the object, and the occlusion of the object are determined, detailed information on action patterns of the object can be obtained in real time.

Claims

1-18. -18. (canceled)

19. A method of tracking an object comprising:

detecting predetermined object feature points in a previous frame;

estimating object feature points of a present frame by using a difference between a pixel value of the previous frame and a pixel value of the present frame;

estimating whether the estimated object feature points of the present frame are occluded on the basis of a similarity between the estimated object feature points of the present frame and the detected object feature points of the previous frame; and

tracking the object based on the detected object feature points of the previous frame corresponding to the occluded object feature points of the present frame when the estimated object feature points of the present frame are occluded.

20. The method of claim 19, wherein the object feature points of the present frame are estimated by calculating an optical flow depending on the detected object feature points on the basis of the difference between the pixel value of the previous frame and the pixel value of the present frame and using the calculated optical flow.

21. The method of claim 20, wherein the optical flow is calculated by calculating the difference between the pixel value of the previous frame and the pixel value of the present frame based on an LKT (Lucas-Kanade-Tomasi) algorithm, and the object feature points are estimated by using the calculated optical flow.

22. The method of claim 19, wherein the object feature point is detected by using AAMs (Active Appearance Models) or ASMs (Active Shape Models).

23. A non-transitory computer-readable recording medium having embodied thereon a computer program for executing the method of claim 19.

24. A method of calculating object pose information comprising:

detecting predetermined object feature points in a previous frame;

estimating object feature point of a present frame by using a difference between a pixel value of the previous frame and a pixel value of the present frame;

calculating object pose information by using coordinate values detected in the previous frame and coordinate values of the object feature points estimated in the present frame;

calculating an affine transformation between coordinate values of object feature points detected in the previous frame and coordinate values of object feature points detected in the present frame;

calculating the object feature points of the present frame by using the coordinate values of the feature points of the object detected in the previous frame and the calculated affine transformation; and

calculating a similarity between the estimated object feature points of the present frame and the calculated object feature points and estimating whether the object feature points are occluded on the basis of the calculated similarity,

wherein the object pose information further includes the occlusion information on whether the object feature points in the present frame are occluded.

25. The method of claim 24, wherein the object feature points are estimated by estimating 2D (X-Y) locations of the object feature points, and the object pose information is calculated by further considering a 3D model of a general object and a table in which information (Z) on heights of the general object is stored.

26. The method of claim 24, wherein the object pose information includes rotation matrix information on directions of the object.

27. The method of claim 24, wherein the object pose information further includes occlusion information on whether the object feature points are occluded in the present frame.

28. The method of claim 24, wherein the object feature points includes feature points related to eyes and a mouth selected from a human face, and the object pose information further includes information on blinking of the eyes or information on a shape of the mouth as pattern information.

29. The method of claim 24,

wherein the object feature points include feature points related to an eye selected from a human face,

wherein the method of calculating object pose information further comprises calculating an edge of an eye region by applying a Sobel operator to the present frame and the previous frame, generating a gradient map depending on the calculated edge of the eye region, and

estimating whether the eyes blink according to a similarity or correlation between the gradient map of the present frame and the gradient map of the previous frame, and wherein the object pose information further includes the pattern information on the blinking of the eyes.

30. The method of claim 24,

wherein the object feature points include feature points related to a mouth selected from a human face,

wherein the method of calculating object pose information further comprises converting a predetermined image including the feature points related to the mouth into a binary level image and estimating an ellipse function that represents an edge of the mouth by using an ellipse fitting process, and

wherein the object pose information further includes the pattern information on the shape of the mouth according to the estimated ellipse function.

31. A non-transitory computer-readable recording medium having embodied thereon a computer program for executing the method of claim 24.

32. An apparatus for tracking a video image object, including at least one processing device, the apparatus comprising:

an initialization unit which initializes, using the at least one processing device, a tracking process by detecting object feature points from a video image;

a first tracking unit which estimates object feature points of a present frame by using a difference between a pixel value of a previous frame and a pixel value of the present frame; and

a second tracking unit which estimates whether the estimated object feature points of the present frame are occluded on the basis of a similarity between the estimated object feature points of the present frame and the detected object feature points of the previous frame; and

tracks the object based on the detected object feature points of the previous frame corresponding to the occluded object feature points of the N˜

th frame when the estimated object feature points of the present frame are occluded.

33. An apparatus for calculating object pose information, including at least one processing device, the apparatus comprising:

an initialization unit which initializes, using the at least one processing device, a tracking process by detecting object feature points from a video image;

a first tracking unit which estimates object feature points of a present frame by using a difference between a pixel value of a previous frame and a pixel value of the present frame;

an object pose information calculation unit which calculates object pose information by using coordinate values of the object feature points estimated by initializing the tracking process and coordinate values of the object feature points of the present frame,

wherein the object pose calculation unit includes a pose estimation unit generating rotation matrix information on directions of the object, an occlusion estimation unit generating occlusion information on whether the object feature points are occluded, and a pattern estimation unit generating pattern information on blinking of eyes and a shape of a mouth.