Adaptive learnable pooling for overlapping camera features

A method for processing image data, the method comprising:

• extracting features from a respective image from each camera of a plurality of cameras;
• and
• fusing the features into a fused image having a grid structure, wherein fusing the features comprises:
• determining a contribution of the respective image from each of the plurality of cameras to a respective cell of the grid structure, and
• aggregating, based on the contribution to the respective cell and a respective set of learnable parameters for each cell, the features from each of the respective images to each respective cell of the fused image to generate aggregated features, wherein the set of learnable parameters comprises a unique weight assigned to each camera of the plurality of cameras that contributes to the respective cell.

A non-transitory computer-readable storage medium storing instructions that, when executed, cause one or more processors to:

• extract features from a respective image from each camera of a plurality of cameras;
• and
• fuse the features into a fused image having a grid structure, wherein to fuse the features, the instructions further cause the one or more processors to:
• determine a contribution of the respective image from each of the plurality of cameras to a respective cell of the grid structure, and
• aggregate, based on the contribution to the respective cell and a respective set of learnable parameters for each cell, the features from each of the respective images to each respective cell of the fused image to generate aggregated features, wherein the set of learnable parameters comprises a unique weight assigned to each camera of the plurality of cameras that contributes to the respective cell.

An apparatus configured for processing image data, the apparatus comprising:

• means for extracting features from a respective image from each camera of a plurality of cameras;
• and
• means for fusing the features into a fused image having a grid structure, wherein the means for fusing the features comprises:
• means for determining a contribution of the respective image from each of the plurality of cameras to a respective cell of the grid structure, and
• means for aggregating, based on the contribution to the respective cell and a respective set of learnable parameters for each cell, the features from each of the respective images to each respective cell of the fused image to generate aggregated features, wherein the set of learnable parameters comprises a unique weight assigned to each camera of the plurality of cameras that contributes to the respective cell.