Method and system for pose estimation

Abstract

The disclosures relate to a method and a system for pose estimation. The method comprises: extracting a plurality of sets of part-feature maps from an image, each set of the extracted part-feature maps encoding the messages for a particular body part and forming a node of a part-feature network; passing a message of each set of the extracted part-feature maps through the part-feature network to update the extracted part-feature maps, resulting in each set of the extracted part-feature maps incorporating the message of upstream nodes; estimating, based on the updated part-feature maps, the body part within the image.

What is claimed is: 
     
         1 . A method for pose estimation, comprising:
 extracting a plurality of sets of part-feature maps from an image, each set of the extracted part-feature maps representing a body part and forming a node of a part-feature network;   passing a message of each set of the extracted part-feature maps through the part-feature network to update the extracted part-feature maps, resulting in each set of the extracted part-feature maps incorporating the message of upstream nodes; and   estimating, based on the updated part-feature maps, the body part within the image.   
     
     
         2 . The method of  claim 1 , wherein
 the passing of the message is performed twice in opposite directions and each pairs of the updated part-feature maps performed in different directions are combined into a score map; and   the estimating of the body part is performed based on the combined score maps.   
     
     
         3 . The method of  claim 1 , wherein the extracting of the part-feature maps is performed via a CNN. 
     
     
         4 . The method of  claim 3 , wherein the CNN is a VGG net. 
     
     
         5 . The method of  claim 4 , wherein three pooling layers are adopted in the VGG net. 
     
     
         6 . The method of  claim 1 , wherein the passing of the message is performed by a convolution operation with a geometrical transformation kernel. 
     
     
         7 . A system for pose estimation, comprising:
 a memory that stores executable components; and   a processor electrically coupled to the memory to execute the executable components for:
 extracting a plurality of sets of part-feature maps from an image, each set of the extracted part-feature maps representing a body part and forming a node of a part-feature network; 
 passing a message of each set of the extracted part-feature maps through the part-feature network to update the extracted part-feature maps, resulting in each set of the extracted part-feature maps incorporating the message of previously passed nodes; and 
 estimating, based on the refined part-feature maps, the body part within the image. 
   
     
     
         8 . The system of  claim 7 , wherein
 the passing of the message is performed twice in opposite directions and each pairs of the updated part-feature maps performed in different directions are combined into a score map; and   the estimating of the body part is performed based on the combined score maps.   
     
     
         9 . The system of  claim 7 , wherein the extracting of the part-feature maps is performed via a CNN. 
     
     
         10 . The system of  claim 9 , wherein the CNN is a VGG net. 
     
     
         11 . The system of  claim 10 , wherein three pooling layers are adopted in the VGG net. 
     
     
         12 . The system of  claim 7 , wherein the passing of the message is performed by a convolution operation with a geometrical transformation kernel.