High-performance sensor fusion architecture

A method of object detection comprising the steps of:

• receiving a stereoscopic pair of images of an area occupied by at least one object;
• extracting image features from the images, with at least a portion of the image features being extracted by the steps of:
• detecting pattern regions and non-pattern regions within each of the pair of images using a texture filter;
• generating an initial estimate of spatial disparities between the pattern regions within each of the pair of images;
• using the initial estimate to generate a subsequent estimate of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using disparity constraints;
• iteratively using the subsequent estimate as the initial estimate in the step of using the initial estimate to generate a subsequent estimate in order to generate further subsequent estimates of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using the disparity constraints until there is no change between the results of subsequent iterations, thereby generating a final estimate of the spatial disparities;
• generating a disparity map of the area occupied by at least one object from the final estimate of the spatial disparities;
• classifying the image features to produce object class confidence data, with at least a portion of the classifying being performed by processing the disparity map with at least one classification algorithm to produce object class confidence data;
• and
• performing data fusion on the object class confidence data to produce a detected object estimate.

A computer program product for object detection, the computer program product comprising means, stored on a computer readable medium, for:

• receiving images of an area occupied by at least one object;
• extracting image features including wavelet features from the images;
• classifying the image features to produce object class confidence data;
• and
• performing data fusion on the object class confidence data to produce a detected object estimate.

A computer program product for object detection, the computer program product comprising means, stored on a computer readable medium, for:

• receiving a stereoscopic pair of images of an area occupied by at least one object;
• extracting image features from the images, with at least a portion of the image features being extracted by means for:
• detecting pattern regions and non-pattern regions within each of the pair of images using a texture filter;
• generating an initial estimate of spatial disparities between the pattern regions within each of the pair of images;
• using the initial estimate to generate a subsequent estimate of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using disparity constraints;
• iteratively using the subsequent estimate as the initial estimate in the means for using the initial estimate to generate a subsequent estimate in order to generate further subsequent estimates of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using the disparity constraints until there is no change between the results of subsequent iterations, thereby generating a final estimate of the spatial disparities;
• generating a disparity map of the area occupied by at least one object from the final estimate of the spatial disparities;
• classifying the image features to produce object class confidence data, with at least a portion of the classifying being performed by processing the disparity map with at least one classification algorithm to produce object class confidence data;
• and
• performing data fusion on the object class confidence data to produce a detected object estimate.

An apparatus for object detection comprising a computer system including a processor, a memory coupled with the processor, an input coupled with the processor for receiving images, and an output coupled with the processor for outputting information based on an object estimation, wherein the computer system further comprises means, residing in its processor and memory, for:

• receiving images of an area occupied by at least one object;
• extracting image features including wavelet features from the images;
• classifying the image features to produce object class confidence data;
• and
• performing data fusion on the object class confidence data to produce a detected object estimate.

An apparatus for object detection comprising a computer system including a processor, a memory coupled with the processor, an input coupled with the processor for receiving images, and an output coupled with the processor for outputting information based on an object estimation, wherein the computer system further comprises means, residing in its processor and memory, for:

• receiving a stereoscopic pair of images of an area occupied by at least one object;
• extracting image features from the images, with at least a portion of the image features being extracted by means for:
• detecting pattern regions and non-pattern regions within each of the pair of images using a texture filter;
• generating an initial estimate of spatial disparities between the pattern regions within each of the pair of images;
• using the initial estimate to generate a subsequent estimate of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using disparity constraints;
• iteratively using the subsequent estimate as the initial estimate in the means for using the initial estimate to generate a subsequent estimate in order to generate further subsequent estimates of the spatial disparities between the non-pattern regions based on the spatial disparities between the pattern regions using the disparity constraints until there is no change between the results of subsequent iterations, thereby generating a final estimate of the spatial disparities;
• generating a disparity map of the area occupied by at least one object from the final estimate of the spatial disparities;
• classifying the image features to produce object class confidence data, with at least a portion of the classifying being performed by processing the disparity map with at least one classification algorithm to produce object class confidence data;
• and
• performing data fusion on the object class confidence data to produce a detected object estimate.