System and method for occluding contour detection
A system method for occluding contour detection using a fully convolutional neural network is disclosed. A particular embodiment includes: receiving an input image; producing a feature map from the input image by semantic segmentation; applying a Dense Upsampling Convolution (DUC) operation on the feature map to produce contour information of objects and object instances detected in the input image; and applying the contour information onto the input image.
1. A system comprising:
a data processor; and
an occluding object contour detection processing module, executable by the data processor, the occluding object contour detection processing module being configured to perform an occluding object contour detection operation using a fully convolutional neural network and dense upsampling convolution (DUC), the occluding object contour detection operation being configured to:
receive an input image;
produce a feature map from the input image by semantic segmentation;
apply a DUC operation on the feature map to produce contour information of objects and object instances detected in the input image; and
apply the contour information onto the input image.
2. The system of claim 1 wherein the semantic segmentation and DUC operation are machine learnable.
3. The system of claim 1 wherein the semantic segmentation is performed by a deep convolutional neural network trained on a dataset configured for a traffic environment.
4. The system of claim 1 wherein the DUC operation is configured operate within a fully convolutional network (FCN).
5. The system of claim 1 wherein the contour information is produced without the use of bounding boxes.
6. The system of claim 1 wherein the contour information is used by an autonomous control subsystem to control a vehicle without a driver.
7. A method comprising:
receiving an input image;
producing a feature map from the input image by semantic segmentation;
applying a Dense Upsampling Convolution (DUC) operation on the feature map to produce contour information of objects and object instances detected in the input image; and
applying the contour information onto the input image.
8. The method of claim 7 wherein the semantic segmentation and DUC operation are machine learnable.
9. The method of claim 7 wherein the semantic segmentation is performed by a deep convolutional neural network trained on a dataset configured for a traffic environment.
10. The method of claim 7 wherein the DUC operation is configured operate within a fully convolutional network (FCN).
11. The method of claim 7 wherein the contour information is produced without the use of bounding boxes.
12. The method of claim 7 wherein the contour information is used by an autonomous control subsystem to control a vehicle without a driver.
13. A non-transitory machine-useable storage medium embodying instructions which, when executed by a machine, cause the machine to:
receive an input image;
produce a feature map from the input image by semantic segmentation;
apply a DUC operation on the feature map to produce contour information of objects and object instances detected in the input image; and
apply the contour information onto the input image.
14. The non-transitory machine-useable storage medium of claim 13 wherein the semantic segmentation and DUC operation are machine learnable.
15. The non-transitory machine-useable storage medium of claim 13 wherein the semantic segmentation is performed by a deep convolutional neural network trained on a dataset configured for a traffic environment.
16. The non-transitory machine-useable storage medium of claim 13 wherein the DUC operation is configured operate within a fully convolutional network (FCN).
17. The non-transitory machine-useable storage medium of claim 13 wherein the contour information is produced without the use of bounding boxes.
18. The non-transitory machine-useable storage medium of claim 13 wherein the contour information is used by an autonomous control subsystem to control a vehicle without a driver.