Estimator training method and pose estimating method using depth image
US-9449392-B2 · Sep 20, 2016 · US
US9613273B2 · US · B2
| Field | Value |
|---|---|
| Publication number | US-9613273-B2 |
| Application number | US-201514716661-A |
| Country | US |
| Kind code | B2 |
| Filing date | May 19, 2015 |
| Priority date | May 19, 2015 |
| Publication date | Apr 4, 2017 |
| Grant date | Apr 4, 2017 |
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An object tracking device includes a short-term processing portion and a long short-term processing portion that are implemented by circuitry and work in a collaborative manner to track an object. The short-term processing portion includes a filter that tracks the object based on short-term memory and spatiotemporal consistency. The long short-term processing portion performs key-point matching-tracking and estimation based on a key-point database in order to track the object. A controller determines an output of the object tracking device based on the processing conducted by the short-term and long short-term processing portions of the tracking device, respectively.
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What is claimed is: 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
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