公开(公告)号：US09747527B2

公开(公告)日：2017-08-29

申请号：US14029633

申请日：2013-09-17

Applicant: NVIDIA CORPORATION

Inventor： Mateusz Jerzy Baranowski ,  Shalini Gupta ,  Elif Albuz

IPC: G06K9/62 ,  G06K9/00

CPC classification number: G06K9/6285 ,  G06K9/00986 ,  G06K9/6256 ,  G06K9/6282

Abstract: In one embodiment of the present invention, a graphics processing unit (GPU) is configured to detect an object in an image using a random forest classifier that includes multiple, identically structured decision trees. Notably, the application of each of the decision trees is independent of the application of the other decision trees. In operation, the GPU partitions the image into subsets of pixels, and associates an execution thread with each of the pixels in the subset of pixels. The GPU then causes each of the execution threads to apply the random forest classifier to the associated pixel, thereby determining a likelihood that the pixel corresponds to the object. Advantageously, such a distributed approach to object detection more fully leverages the parallel architecture of the PPU than conventional approaches. In particular, the PPU performs object detection more efficiently using the random forest classifier than using a cascaded classifier.

公开(公告)号：US09971959B2

公开(公告)日：2018-05-15

申请号：US14029640

申请日：2013-09-17

Applicant: NVIDIA CORPORATION

Inventor： Mateusz Jerzy Baranowski ,  Shalini Gupta ,  Elif Albuz

IPC: G06K9/62 ,  G06K9/00

CPC classification number: G06K9/6285 ,  G06K9/00986 ,  G06K9/6256 ,  G06K9/6282

Abstract: In one embodiment of the present invention, a graphics processing unit (GPU) is configured to detect an object in an image using a random forest classifier that includes multiple, identically structured decision trees. Notably, the application of each of the decision trees is independent of the application of the other decision trees. In operation, the GPU partitions the image into subsets of pixels, and associates an execution thread with each of the pixels in the subset of pixels. The GPU then causes each of the execution threads to apply the random forest classifier to the associated pixel, thereby determining a likelihood that the pixel corresponds to the object. Advantageously, such a distributed approach to object detection more fully leverages the parallel architecture of the parallel processing unit (PPU) than conventional approaches. In particular, the PPU performs object detection more efficiently using the random forest classifier than using a cascaded classifier.