Apparatus and method for object tracking

1. An object tracking device comprising:

a first tracker implemented by circuitry, the circuitry configured to:

determine and extract features, of a candidate image patch from an input image frame,

evaluate the candidate image patch and cyclically shifted image patches of the candidate image patch, to determine a location of a tracked object;

resize and extract features from a predetermined number of image patches surrounding the determined location to obtain a first instance of the tracked object;

a second tracker implemented by circuitry, the circuitry configured to:

detect a plurality of key-points from the input image frame,

classify, based on a key-point database, each detected key-point as one of a matched target key-point, a matched background key-point, and an unmatched key-point,

compute, based on consecutive input image frames, an active set of key-points,

estimate, based on the computed active set of key-points and the matched target key-points, a set of inlier key-points included in a target bounding box that corresponds to a second instance of the tracked object; and

a controller implemented by circuitry, the circuitry being configured to generate a tracked object based on a comparison of the first instance of the tracked object and the second instance of the tracked object.

2. The object tracking device of claim 1 , wherein the determined location of the tracked object has a highest filtering score, and the first instance of the tracked object is included in an image patch having a highest filter response.

3. The object tracking device of claim 2 , wherein the circuitry is further configured to:

update the filter coefficients and an object template in an interpolating fashion at a predetermined update rate.

4. The object tracking device of claim 1 , wherein the extracted features are based on a histogram of oriented gradient descriptors and color attributes, and the detected key-points are characterized by scale-invariant-feature-transform descriptors.

5. The object tracking device of claim 1 , wherein the circuitry of the controller is configured to classify the detected key-point as a matched target key-point based on a matching confidence of the detected key-point and a first neighbor of the detected keypoint that belongs to the key-point database being above a first threshold.

6. The object tracking device of claim 5 , wherein the circuitry of the controller is further configured to classify the detected key-point as a matched target key-point based on a ratio of distance parameter being below a second threshold, the ratio of distance parameter being a ratio of the Euclidean distance between the detected key-point and the first nearest neighbor to the Euclidean distance between the detected key-point and the second nearest neighbor.

7. The object tracking device of claim 5 , wherein the matching confidence is a cosine similarity between the detected key-point and the first nearest neighbor of the detected key-point.

8. The object tracking device of claim 1 , wherein the active set of key-points includes key-points whose displacement between the consecutive input image frames is less than a predetermined threshold.

9. The object tracking device of claim 1 , wherein the circuitry of the controller is further configured to

compute an intersection-over-union (IOU) of the first instance of the tracked object and the second instance of the tracked object; and

determine whether the first instance of the tracked object and the second instance of the tracked object are consistent based on the computed IOU and a predetermined consistency threshold.

10. The object tracking device of claim 1 , wherein the circuitry of the controller is further configured to:

update the active set of key-points for each input image frame by determining a redundant set of key-points within the active set of key-points, the redundant set of key-points being computed based on quantization ID's of the key-points; and

update the key-point database based on a number of inlier key-points being greater than a predetermined threshold and a number of matched background key-points lying within the target bounding box being zero.

11. The object tracking device of claim 10 , wherein a timespan corresponding to an amount of time the key-points are maintained in the key-point database is exponentially distributed based on a relative strength parameter of the key-point database, a period of a forgetting parameter, and a scale of a timespan parameter.

12. A method of object tracking comprising:

determining, and extracting features of a candidate image patch, from an input image frame;

evaluating by circuitry, the candidate image patch and cyclically shifted image patches of the candidate image patch, to determine a location of a tracked object;

resizing, and extracting features, from a predetermined number of image patches surrounding the determined location to obtain a first instance of the tracked object;

detecting a plurality of key-points from the input image frame;

classifying, based on a key-point database, each detected key-point as one of a matched target key-point, a matched background key-point, and an unmatched key-point,

computing by circuitry, based on consecutive input image frames, an active set of key-points;

estimating, based on the computed active set of key-points and the matched target key-points, a set of inlier key-points included in a target bounding box that corresponds to a second instance of the tracked object; and

generating by circuitry, a tracked object based on a comparison of the first instance of the tracked object and the second instance of the tracked object.

13. The method of object tracking of claim 12 , wherein the determined location of the tracked object has a highest filtering score, and the first instance of the tracked object is included in an image patch having a highest filter response.

14. The method of object tracking of claim 12 , further comprising:

updating the filter coefficients and an object template in an interpolating fashion at a predetermined update rate.

15. The method of object tracking of claim 12 , wherein the circuitry is configured to classify the detected key-point as a matched target key-point based on a matching confidence of the detected key-point and a first neighbor of the detected keypoint that belongs to the key-point database being above a first threshold.

16. The method of object tracking of claim 15 , wherein the circuitry is further configured to classify the detected key-point as a matched target key-point based on a ratio of distance parameter being below a second threshold, the ratio of distance parameter being a ratio of the Euclidean distance between the detected key-point and the first nearest neighbor to the Euclidean distance between the detected key-point and the second nearest neighbor.

17. The method of object tracking of claim 12 , further comprising:

computing by circuitry, an intersection-over-union (IOU) of the first instance of the tracked object and the second instance of the tracked object; and

determining whether the first instance of the tracked object and the second instance of the tracked object are consistent based on the computed IOU and a predetermined consistency threshold.

18. A non-transitory computer readable medium having stored thereon a program that when executed by a computer causes the computer to execute a method to track an object, the method comprising:

determining, and extracting features of a candidate image patch, from an input image frame;

evaluating the candidate image patch and cyclically shifted image patches of the candidate image patch, to determine a location of a tracked object;

resizing and extracting features, from a predetermined number of image patches surrounding the determined location to obtain a first instance of the tracked object;

detecting a plurality of key-points from the input image frame;

classifying, based on a key-point database, each detected key-point as one of a matched target key-point, a matched background key-point, and an unmatched key-point;

computing based on consecutive input image frames, an active set of key-points;

estimating, based on the computed active set of key-points and the matched target key-points, a set of inlier key-points included in a target bounding box that corresponds to a second instance of the tracked object; and

generating a tracked object based on a comparison of the first instance of the tracked object and the second instance of the tracked object.

19. The non-transitory computer readable medium of claim 18 , wherein the determined location of the tracked object has a highest filtering score, and the first instance of the tracked object is included in a first image patch having a highest filter response.

20. The non-transitory computer readable medium of claim 18 , the method further comprising:

computing an intersection-over-union (IOU) of the first instance of the tracked object and the second instance of the tracked object; and

determining whether the first instance of the tracked object and the second instance of the tracked object are consistent based on the computed IOU and a predetermined consistency threshold.

21. The object tracking device of claim 1 , wherein each image patch of the predetermined number of image patches surrounding the determined location has a unique size.