Subcategory-aware convolutional neural networks for object detection
First Claim
1. A computer-implemented method for detecting objects by using subcategory-aware convolutional neural networks (CNNs), the method comprising:
- generating object region proposals from an image by a region proposal network (RPN) which utilizes subcategory information; and
classifying and refining the object region proposals by an object detection network (ODN) that simultaneously performs object category classification, subcategory classification, and bounding box regression,wherein the RPN and the ODN each include a feature extrapolating layer to detect object categories with scale variations among the objects.
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Abstract
A computer-implemented method for detecting objects by using subcategory-aware convolutional neural networks (CNNs) is presented. The method includes generating object region proposals from an image by a region proposal network (RPN) which utilizes subcategory information, and classifying and refining the object region proposals by an object detection network (ODN) that simultaneously performs object category classification, subcategory classification, and bounding box regression. The image is an image pyramid used as input to the RPN and the ODN. The RPN and the ODN each include a feature extrapolating layer to detect object categories with scale variations among the objects.
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Citations
17 Claims
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1. A computer-implemented method for detecting objects by using subcategory-aware convolutional neural networks (CNNs), the method comprising:
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generating object region proposals from an image by a region proposal network (RPN) which utilizes subcategory information; and classifying and refining the object region proposals by an object detection network (ODN) that simultaneously performs object category classification, subcategory classification, and bounding box regression, wherein the RPN and the ODN each include a feature extrapolating layer to detect object categories with scale variations among the objects. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A system for detecting objects by using subcategory-aware convolutional neural networks (CNNs), the system comprising:
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a memory; and a hardware computer processor operatively coupled to the memory, the processor being configured for; generating object region proposals from an image by a region proposal network (RPN) which utilizes subcategory information; and classifying and refining the object region proposals by an object detection network (ODN) that simultaneously performs object category classification, subcategory classification, and bounding box regression, wherein the RPN and the ODN each include a feature extrapolating layer to detect object categories with scale variations among the objects. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A non-transitory computer-readable storage medium comprising a computer-readable program for detecting objects by using subcategory-aware convolutional neural networks (CNNs), wherein the computer-readable program when executed on a computer, using a hardware computer processor coupled to the non-transitory computer-readable storage medium, causes the computer to perform the steps of:
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generating object region proposals from an image by a region proposal network (RPN) which utilizes subcategory information; and classifying and refining the object region proposals by an object detection network (ODN) that simultaneously performs object category classification, subcategory classification, and bounding box regression, wherein the RPN and the ODN each include a feature extrapolating layer to detect object categories with scale variations among the objects. - View Dependent Claims (14, 15, 16, 17)
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Specification