System and method for a deep learning machine for object detection
AssigneeSamsung Electronics Co., Ltd.
InventorsArvind Yedla, Marcel Nassar, Mostafa El-Khamy, Jungwon Lee
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Abstract
Apparatuses and methods of manufacturing same, systems, and methods for object detection using a region-based deep learning model are described. In one aspect, a method is provided, in which a region proposal network (RPN) is used to identify regions of interest (RoI) in an image by assigning a confidence levels, the assigned confidence levels of the RoIs are used to boost the background score assigned by the downstream classifier to each RoI, and the background scores are used in a softmax function to calculate the final class probabilities for each object class.
Claims18
A method of object detection in an image using a region-based deep learning model, the method comprising:
- identifying, using a region proposal network (RPN), regions of interest (RoI) in the image and assigning a confidence levels to each identified RoI;
- boosting a background score assigned by a downstream classifier to each RoI, using the confidence level assigned to the ROI and optical flow magnitude;
- using the boosted background scores in a softmax function to calculate final class probabilities;
- and
- identifying each RoI as including an object in a foreground of the image or as a part of the background of the image, based on the final class probabilities.
An apparatus capable of object detection using a region-based deep learning model, comprising:
- one or more non-transitory computer-readable media;
- and
- at least one processor which, when executing instructions stored on one or more non-transitory computer readable media, performs the steps of:
- identifying, using a region proposal network (RPN), regions of interest (RoI) in the image and assigning a confidence levels to each identified RoI;
- boosting a background score assigned by a downstream classifier to each RoI, using the confidence level assigned to the ROI and optical flow magnitude;
- using the boosted background scores in a softmax function to calculate final class probabilities;
- and
- identifying each RoI as including an object in a foreground of the image or as a part of the background of the image, based on the final class probabilities.
A method, comprising:
- manufacturing a chipset comprising:
- at least one processor which, when executing instructions stored on one or more non-transitory computer readable media, performs the steps of:
- identifying, using a region proposal network (RPN), regions of interest (RoI) in the image and assigning a confidence levels to each identified RoI;
- boosting a background score assigned by a downstream classifier to each RoI, using the confidence level assigned to the ROI and optical flow magnitude;
- using the boosted background scores in a softmax function to calculate final class probabilities;
- and
- identifying each RoI as including an object in a foreground of the image or as a part of the background of the image, based on the final class probabilities, and
- the one or more non-transitory computer-readable media which store the instructions.
A method of testing an apparatus, comprising:
- testing whether the apparatus has at least one processor which, when executing instructions stored on one or more non-transitory computer readable media, performs the steps of:
- identifying, using a region proposal network (RPN), regions of interest (RoI) in the image and assigning a confidence levels to each identified RoI;
- boosting a background score assigned by a downstream classifier to each RoI, using the confidence level assigned to the ROI and optical flow magnitude;
- using the boosted background scores in a softmax function to calculate final class probabilities;
- and
- identifying each RoI as including an object in a foreground of the image or as a part of the background of the image, based on the final class probabilities, and
- testing whether the apparatus has the one or more non-transitory computer-readable media which store the instructions.
Dependencies are inferred from the claim text.