Object detection based on joint feature extraction
First Claim
Patent Images
1. A computer-implemented method comprising:
- characterizing, by at least one processor, at least one first feature from an image, including by extracting via a first feature extraction stage the at least one first feature and characterizing the image in a whole scale providing context around object regions;
identifying a first candidate object region defined by a first bounding box in the image based on the at least one first feature, wherein the first candidate object region includes a candidate face region;
identifying a second candidate object region defined by a second bounding box in the image based on the at least one first feature;
determining whether the first candidate object region defined by the first bounding box is overlapped by the second candidate object region defined by the second bounding box;
responsive to a determination that the first candidate object region defined by the first bounding box is overlapped by the second candidate object region defined by the second bounding box;
retaining the first candidate object region and discarding the second candidate region, andcharacterizing at least one second feature from the first candidate object region based on the at least one first feature, including by extracting via a second feature extraction stage the at least one second feature and characterizing the image in a local scale, which is at the same scale as the whole scale providing information from within the first candidate object region, the first feature extraction stage and the second feature extraction stage trained based on a joint optimization objective to improve accuracy of object detection;
responsive to a determination that the first candidate object region defined by the first bounding box is not overlapped by the second candidate object region defined by the second bounding box;
retaining both the first and second candidate object regions, andextracting at least one second feature at the same scale of the whole scale from the first and second candidate object regions; and
determining, based on the at least one first feature and the at least one second feature, a target object region in the image and a confidence for the determined target object region.
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Abstract
In implementations of the subject matter described herein, a solution for object detection is proposed. First, a feature(s) is extracted from an image and used to identify a candidate object region in the image. Then another feature(s) is extracted from the identified candidate object region. Based on the features extracted in these two stages, a target object region in the image and a confidence for the target object region are determined. In this way, the features that characterize the image from the whole scale and a local scale are both taken into consideration in object recognition, thereby improving accuracy of the object detection.
34 Citations
20 Claims
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1. A computer-implemented method comprising:
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characterizing, by at least one processor, at least one first feature from an image, including by extracting via a first feature extraction stage the at least one first feature and characterizing the image in a whole scale providing context around object regions; identifying a first candidate object region defined by a first bounding box in the image based on the at least one first feature, wherein the first candidate object region includes a candidate face region; identifying a second candidate object region defined by a second bounding box in the image based on the at least one first feature; determining whether the first candidate object region defined by the first bounding box is overlapped by the second candidate object region defined by the second bounding box; responsive to a determination that the first candidate object region defined by the first bounding box is overlapped by the second candidate object region defined by the second bounding box; retaining the first candidate object region and discarding the second candidate region, and characterizing at least one second feature from the first candidate object region based on the at least one first feature, including by extracting via a second feature extraction stage the at least one second feature and characterizing the image in a local scale, which is at the same scale as the whole scale providing information from within the first candidate object region, the first feature extraction stage and the second feature extraction stage trained based on a joint optimization objective to improve accuracy of object detection; responsive to a determination that the first candidate object region defined by the first bounding box is not overlapped by the second candidate object region defined by the second bounding box; retaining both the first and second candidate object regions, and extracting at least one second feature at the same scale of the whole scale from the first and second candidate object regions; and determining, based on the at least one first feature and the at least one second feature, a target object region in the image and a confidence for the determined target object region. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A device comprising:
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a processing unit; a memory coupled to the processing unit and storing instructions thereon, the instructions, when executed by the processing unit, causing the device to; characterize at least one first feature from an image, including by extracting via a first feature extraction stage the at least one first feature and characterizing the image in a whole scale providing context around object regions; identify a first candidate object region defined by a first bounding box in the image based on the at least one first feature, wherein the first candidate object region includes a candidate face region; identify a second candidate object region defined by a second bounding box in the image based on the at least one first feature; determine whether the first candidate object region defined by the first bounding box is overlapped by the second candidate object region defined by the second bounding box; responsive to a determination that the first candidate object region defined by the first bounding box is overlapped by the second candidate object region defined by the second bounding box; retain the first candidate object region and discard the second candidate region, and characterize at least one second feature from the first candidate object region based on the at least one first feature, including by extracting via a second feature extraction stage the at least one second feature and characterizing the image in a local scale, which is at the same scale as the whole scale, providing information from within the first candidate object region, the first feature extraction stage and the second feature extraction stage trained based on a joint optimization objective to improve accuracy of object detection; responsive to a determination that the first candidate object region defined by the first bounding box is not overlapped by the second candidate object region defined by the second bounding box; retain both the first and second candidate object regions, and extract at least one second feature at the same scale of the whole scale from the first and second candidate object regions; and determine, based on the at least one first feature and the at least one second feature, a target object region in the image and a confidence for the determined target object region. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A storage device comprising machine-executable instructions, the instructions, when executed on at least one processor, causing the processor to:
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characterize at least one first feature from an image, including by extracting via a first feature extraction stage the at least one first feature and characterizing the image in a whole scale providing context around object regions; identify a first candidate object region defined by a first bounding box in the image based on the at least one first feature, wherein the first candidate object region includes a candidate face region; identify a second candidate object region defined by a second bounding box in the image based on the at least one first feature; determine whether the first candidate object region defined by the first bounding box is overlapped by the second candidate object region defined by the second bounding box; responsive to a determination that the first candidate object region defined by the first bounding box is overlapped by the second candidate object region defined by the second bounding box; retain the first candidate object region and discard the second candidate region, and characterize at least one second feature from the first candidate object region based on the at least one first feature, including by extracting via a second feature extraction stage the at least one second feature to characterize the image in a local scale, which is at the same scale as the whole scale providing information from within the first candidate object region, the first feature extraction stage and the second feature extraction stage trained based on a joint optimization objective to improve accuracy of object detection; responsive to a determination that the first candidate object region defined by the first bounding box is not overlapped by the second candidate object region defined by the second bounding box; retain both the first and second candidate object regions, and extract at least one second feature at the same scale of the whole scale from the first and second candidate object regions; and determine, based on the at least one first feature and the at least one second feature, a target object region in the image and a confidence for the determined target object region. - View Dependent Claims (20)
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Specification