A METHOD FOR OBJECT TRACKING

• US 20160140727A1
• Filed: 06/17/2013
• Published: 05/19/2016
• Est. Priority Date: 06/17/2013
• Status: Abandoned Application

• Current Assignee: Aselsan Elektronik Sanayi Ve Ticaret Anonim Sirketi
• Original Assignee: Aselsan Elektronik Sanayi Ve Ticaret Anonim Sirket I
• Inventors: YILMAZ, Ozgur
• Application Number:
  US14/899,127
• Publication Number:
  US 20160140727A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06F 18/214 Generating training patterns; Bootstrap methods, e.g. bagging or boosting
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 18/23 Clustering techniques
• CPC Class Codes: G06F 18/24 Classification techniques
• CPC Class Codes: G06T 2207/20081 Training; Learning
• CPC Class Codes: G06T 2207/30232 Surveillance
• 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 7/248 involving reference images or patches
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80

Abstract

The present invention relates to a method for object tracking where the tracking is realized based on object classes, where the classifiers of the objects are trainable without a need for supervision and where the tracking errors are reduced and robustness is increased.

Claims

1. A method for object tracking, comprising the steps of:

S1;

receiving a plurality of coordinates (bounding box) target in an input image from the user,

S2;

determining if an acquired image is the first image acquired or not,

S3;

if the acquired image is the first image acquired then training of a classifier that discriminates target from the background,

S4;

if the acquired image is not the first image acquired then detecting the target using the classifier that is trained in the step,

S5;

determining if the detection is successful or not,

S6;

if the detection is successful then updating the classifier,

S7;

if the detection is unsuccessful for a predefined number of consecutive frames then termination of tracking,

wherein the step of S3 further comprising the sub-steps of;

extracting the feature representation of image patches from an input image,

training a linear classifier,

determining if the change in the classifier is greater than a predefined value,

if the change in the classifier is greater than a predefined value then rejecting the training output,

if the change in the classifier is not greater than a predefined value then updating the classifier,

if the change in the classifier is greater than another predefined value, then saving the original classifier in a database.

3. The method for object tracking of claim 1, the step S4 further comprising the sub-steps of:

S41;

using the current classifier for labeling the target patches that is image patches extracted around the last known location of the target,

S42;

using the classifiers that are in the database for labeling the target patches,

S43;

comparing the number of patches acquired in the steps S41 and S42,

S44;

if using the current classifier for labeling the target patches produces a bigger number of target patches, then using the current classifier as classifier,

S45;

if one of the classifiers that is in the database produces a bigger number of target patches by a predetermined ratio then assigning that classifier in the database as the current classifier,

S46;

determining the putative target pixels, which are the centers of each classified target patch,

S47;

determining clusters of pixels which are classified to be the target,

assigning the cluster center closest to the previously known target center as the correct cluster center.

4. The method for object tracking as in claim 1, wherein the determined position of the target is compared with the position of the target in the previous image frame, and if the difference between the positions of the target is unexpectedly high or more than one target appears in the latter frame, then the tracking is evaluated as inconsistent.

5. The method for object tracking of claim 1, wherein if there are more than one target detected in the latter frame, then the target closest to the position of the target in the previous. flame, is considered the target in question.

6. The method for object tracking of claim 1, wherein multiple instances of the classifier is saved and utilized, providing the tracker an appearance memory.

7. The method for object tracking of claim 1 wherein the trained classifiers are stored in a database so that they can be utilized again during tracking when the target appearance changes.

8. The method for object tracking of claim 1 wherein the classifiers that differ from the previous classifier by more than a predefined value are neglected, providing rejecting false trainings due to tracking errors or occlusions and enhancing robustness.

2. (canceled)

9. The method for object tracking of claim 2, wherein if there are more than one target detected in the latter frame, then the target closest to the position of the target in the previous frame, is considered the target in question.

10. The method for object tracking of claim 2, wherein multiple instances of the classifier are saved and utilized, providing the tracker an appearance memory.