APPARATUS AND METHOD FOR TRACKING IMAGE

• US 20100034464A1
• Filed: 08/05/2009
• Published: 02/11/2010
• Est. Priority Date: 08/05/2008
• Status: Abandoned Application

• Current Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Original Assignee: Kabushiki Kaisha Toshiba (Toshiba Corporation)
• Inventors: Kozakaya, Tatsuo, Takeguchi, Tomoyuki, Ike, Tsukasa, Ito, Satoshi, Kubota, Susumu
• Application Number:
  US12/535,765
• Publication Number:
  US 20100034464A1
• Time in Patent Office: Days
• US Class Current: 382/190
• CPC Class Codes: G06F 18/2115 by evaluating different subsets according to an optimisation criterion, e.g. class separability, forward selection or backward elimination

Abstract

An image processing apparatus includes a classification unit configured to extract N features from an input image using pre-generated N feature extraction units and calculate confidence value which represents object-likelihood based on the extracted N features, an object detection unit configured to detect an object included in the input image based on the confidence value, a feature selection unit configured to select M feature extraction units from the N feature extraction units such that separability between the confidence value of the object and that of background thereof becomes greater than a case where the N feature extraction units are used, the M being a positive integer smaller than N, and an object tracking unit configured to extract M features from the input image and tracks the object using the M features selected by the feature selection unit.

Claims

1. An image processing apparatus, comprising:

a classification unit configured to extract N features from an input image using pre-generated N feature extraction units and calculate confidence value which represents object-likelihood based on the extracted N features;

an object detection unit configured to detect an object included in the input image based on the confidence value;

a feature selection unit configured to select M feature extraction units from the N feature extraction units such that separability between the confidence value of the object and that of background thereof becomes greater than a case where the N feature extraction units are used, the M being a positive integer smaller than N; and

an object tracking unit configured to extract M features from the input image and tracks the object using the M features selected by the feature selection unit.

2. The apparatus of claim 1, wherein the object detection unit calculates the confidence value based on the extracted M features and tracks the object based on the calculated confidence value.

3. The apparatus of claim 1, wherein the object tracking unit calculates the confidence value based on a similarity between a first vector which includes M first features extracted from a position of the object in the input image and a second vector which includes M second features extracted from a position of the object in the input image for which detection of the object detection unit or tracking of the object tracking unit is completed.

4. The apparatus of claim 3, wherein the similarity is calculated by a rate where a sign of each component of the first vector is equal to a sign of each corresponding component of the second vector.

5. The apparatus of claim 2, further comprising a control unit configured to calculate the confidence value at each position of the input image and determine that a peak of the confidence value is a position of the object.

6. The apparatus of claim 5, wherein the control unit determines that detection of the object is unsuccessful when a value at the peak of the confidence value is smaller than a threshold value.

7. The apparatus of claim 5, wherein the control unit calculates the confidence value at each position of the input image and determines that a peak of the confidence value is a position of the object to be tracked.

8. The apparatus of claim 7, wherein the control unit determines that tracking of the object is unsuccessful when a value at the peak of the confidence value is smaller than a threshold value and detects the object by the object detection unit again.

9. The apparatus of claim 1, wherein the feature selection unit generates a plurality of groups of features, where each of the groups contains the extracted N features, based on a detection result of the object detection unit or a tracking result of the object tracking unit and selects M feature extraction units from the N feature extraction units such that separability between the confidence value of the object and that of background thereof becomes greater.

10. The apparatus of claim 9, wherein the feature selection unit generates a plurality of groups of features, where each of the groups contains the extracted N features, from a neighboring area of the detected or tracked object and generates a plurality of groups of features, where each of the groups contains the extracted N features, from a neighboring area of the object.

11. The apparatus of claim 10, wherein the feature selection unit selects M feature extraction units from the N feature extraction units such that separability between the confidence value of the object and that of the neighboring area becomes greater.

12. The apparatus of claim 9, wherein the feature selection unit stores, as a history, the features of the plurality of groups generated in one or more images, where detection or tracking of the object is completed, and positions of the features of the plurality of groups on the images.

13. The apparatus of claim 12, wherein the feature selection unit selects M feature extraction units from the N feature extraction units such that separability between the object and the background thereof becomes greater based on the history.

14. A computer-implemented image processing method, comprising:

extracting N features from an input image using pre-generated N feature extraction units and calculating confidence value which represents object-likelihood based on the extracted N features;

detecting an object included in the input image based on the confidence value;

selecting M feature extraction units from the N feature extraction units such that separability between the confidence value of the object and that of background thereof becomes greater than a case where the N feature extraction units are used, the M being a positive integer smaller than N; and

extracting M features from the input image and tracking the object using the selected M features.

15. An image processing program stored in a computer readable storage medium for causing a computer to implement a instruction, the instruction comprising:

extracting N features from an input image using pre-generated N feature extraction units and calculating confidence value which represents object-likelihood based on the extracted N features;

detecting an object included in the input image based on the confidence value;

selecting M feature extraction units from the N feature extraction units such that separability between the confidence value of the object and that of background thereof becomes greater than a case where the N feature extraction units are used, the M being a positive integer smaller than N; and

extracting M features from the input image and tracking the object using the selected M features.