Method of Tracking Objects in a Video Sequence

• US 20080181453A1
• Filed: 03/01/2006
• Published: 07/31/2008
• Est. Priority Date: 03/17/2005
• Status: Abandoned Application

• Current Assignee: British Telecommunications PLC (BT Group PLC)
• Original Assignee: British Telecommunications PLC (BT Group PLC)
• Inventors: Xu, Li-Qun, Folch, Pere P
• Application Number:
  US11/886,167
• Publication Number:
  US 20080181453A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06T 2207/10016 Video; Image sequence
• CPC Class Codes: G06T 7/194 involving foreground-background segmentation
• CPC Class Codes: G06T 7/215 Motion-based segmentation
• CPC Class Codes: G06T 7/251 involving models
• CPC Class Codes: G06T 7/277 involving stochastic approaches, e.g. using Kalman filters

Abstract

A video surveillance system (10) comprises a camera (25), a personal computer (PC) (27) and a video monitor (29). Video processing software is provided on the hard disk drive of the PC (27). The software is arranged to perform a number of processing operations on video data received from the camera, the video data representing individual frames of captured video. In particular, the software is arranged to identify one or more foreground blobs in a current frame, to match the or each blob with an object identified in one or more previous frames, and to track the motion of the or each object as more frames are received. In order to maintain the identity of objects during an occlusion event, an appearance model is generated for blobs that are close to one another in terms of image position. Once occlusion takes place, the respective appearance models are used to segment the resulting group blob into regions which are classified as representing one or other of the merged objects.

Claims

1. A method of tracking objects in a video sequence comprising a plurality of frames, the method comprising:

(a) receiving a first frame including a plurality of candidate objects and identifying therein first and second candidate objects whose respective frame positions are within a predetermined distance of each other;

(b) providing first and second appearance models representative of the respective first and second candidate objects;

(c) receiving a second, subsequent, frame including one or more new candidate objects and identifying therefrom a group candidate object resulting from the merging of the first and second candidate objects identified in (a); and

(d) identifying, using the first and second appearance models, regions of the group candidate object which respectively correspond to the first and second candidate objects.

2. A method according to claim 1, wherein prior to step (c), the method comprises comparing each of the candidate objects in the first frame with an object identified in a previous frame to determine if there is a correspondence therebetween.

3. A method according to claim 2, wherein each candidate object has an associated set of template data representative of a plurality of features of said candidate object, the comparing step comprising applying in a cost function the template data of (i) a candidate object in the first frame, and (ii) an object identified in a previous frame, thereby to generate a numerical parameter from which it can be determined whether there is a correspondence between said candidate object and said object identified in the previous frame.

4. A method according to claim 3, wherein the cost function is given by:

$D<br><br>\left(l,k\right)=\sqrt{\underset{l=1}{\overset{N}{\sum <br><br>}}<br><br>\frac{{\left(x_{\mathrm{li}}-y_{\mathrm{ki}}\right)}^2}{{\mathrm{\sigma <br><br>}}_{\mathrm{li}}^2}}$

where y_ki represents a feature of the candidate object identified in the first frame, x_li represents a feature of the candidate object identified in one or more previous frames, σ

_li² is the variance of x_li, over a predetermined number of frames, and N is the number of features represented by the set of template data.

5. A method according to claim 1, wherein the group candidate object is defined by a plurality of group pixels, step (d) comprising determining, for each group pixel, which of the first and second candidate objects said group pixel is most likely to correspond using a predetermined likelihood function dependent on each of the first and second appearance models.

6. A method according to claim 5, wherein the first and second appearance models represent the respective colour distribution of the first and second candidate objects.

7. A method according to claim 5, wherein the first and second appearance models represent of a combination of the respective (a) colour distribution of, and (b) edge density information for, the first and second candidate objects.

8. A method according to claim 7, wherein the edge density information is derived from a Sobel edge detection operation performed on the candidate object.

9. A method according to claim 5, wherein the likelihood function is further dependent on a spatial affinity metric (SAM) representative of said group pixel'"'"'s position with respect to a predetermined reference position of the group candidate object.

10. A method according to claim 5, wherein the likelihood function is further dependent on a depth factor indicative of the relative depth of the first and second candidate objects with respect to a viewing position.

11. A method according to claim 1, wherein step (c) comprises identifying a new candidate object whose frame position partially overlaps the respective frame positions of the first and second candidate objects identified in (a).

12. A method according to claim 1, wherein step (c) comprises identifying that the number of candidate objects in the second frame is less than the number of candidate objects identified in the first frame, and identifying a new candidate object whose frame position partially overlaps the respective frame positions of the first and second candidate objects identified in (a).

15. A computer program stored on a computer usable medium, the computer program being arranged, when executed on a processing device, to perform the steps defined in claim 1.

13. A method of tracking objects in a video sequence comprising a plurality of frames, the method comprising:

(a) receiving a first frame including a plurality of candidate objects and identifying therefrom at least two candidate objects whose respective frame positions are within a predetermined distance of one another;

(b) providing an appearance model for each candidate object identified in step (a), the appearance model representing the distribution of appearance features within the respective candidate object;

(c) receiving a second, subsequent, frame and identifying therein a group candidate object resulting from the merging of said at least two candidate objects;

(d) segmenting said group candidate object into regions corresponding to said at least two candidate objects based on analysis of their respective appearance models and an appearance model representative of the group candidate object; and

(e) assigning a separate tracking identity to each region of the group candidate object.

14. A method of tracking objects in a video sequence comprising a plurality of frames, the method comprising:

(a) in a first frame, identifying a plurality of candidate objects and identifying therein first and second candidate objects whose respective frame positions are within a predetermined distance of each other;

(b) providing first and second appearance models representing the distribution of appearance features within the respective first and second candidate objects;

(c) in a second frame, identifying a group candidate object resulting from the merging of the first and second candidate objects identified in (a); and

(d) classifying the group candidate into regions corresponding to the first and second candidate objects based on analysis of their respective appearance models.

16. An image processing system comprising:

means arranged to receive image data representing frames of an image sequence;

data processing means arranged to;

(i) identify, in a first frame, first and second candidate objects whose respective frame positions are within a predetermined distance of each other;

(ii) provide first and second appearance models representing the distribution of appearance features within the respective first and second candidate objects;

(iii) identify, in a second frame, a group candidate object resulting from the merging of the first and second candidate objects identified in (i); and

(iv) classify the group candidate into regions corresponding to the first and second candidate objects based on analysis of their respective appearance models.

17. A video surveillance system comprising:

a video camera arranged to provide image data representing sequential frames of a video sequence; and

an image processing system according to claim 16.