Framebuffer-less system and method of convolutional neural network
AssigneeHimax Technologies Limited
InventorDer-Wei Yang
Life of this patent
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Abstract
A framebuffer-less system of convolutional neural network (CNN) includes a region of interest (ROI) unit that extracts features, according to which a region of interest in an input image frame is generated; a convolutional neural network (CNN) unit that processes the region of interest of the input image frame to detect an object; and a tracking unit that compares the features extracted at different times, according to which the CNN unit selectively processes the input image frame.
Claims15
A framebuffer-less system of convolutional neural network (CNN), comprising:
- a region of interest (ROI) unit that extracts features, according to which a region of interest in an input image frame is generated;
- a convolutional neural network (CNN) unit that processes the region of interest of the input image frame to detect an object;
- a tracking unit that compares features extracted at different times, according to which the CNN unit selectively processes the input image frame;
- and
- a temporary storage for storing the features extracted by the ROI unit;
- wherein the ROI unit adopts scan-line based technique and block-based scheme to find the region of interest in the input image frame, which is divided into a plurality of blocks of image;
- wherein the ROI unit comprises:
- a feature extractor that extracts the features from the input image frame;
- and
- a classifier that makes decision whether to perform CNN for each block of image, thus generating a decision map, according to which the region of interest is determined.
A framebuffer-less method of convolutional neural network (CNN), comprising:
- extracting features to generate a region of interest (ROI) in an input image frame;
- performing convolutional neural network (CNN) on the region of interest of the input image frame to detect an object;
- and
- comparing features extracted at different times and accordingly processing the input image frame selectively;
- wherein the ROI is generated by adopting scan-line based technique and block-based scheme, the input image frame being divided into a plurality of blocks of image;
- wherein the step of generating the ROI comprises:
- extracting the features from the input image frame;
- and
- making decision by classification whether to perform CNN for each block of image, thus generating a decision map, according to which the region of interest is determined.
Dependencies are inferred from the claim text.