US 12,482,246 B2
Object detection system and method for updating cartesian representation of region of interest
Robert De Temple, Essen (DE)
Assigned to CRON AI LTD., London (GB)
Filed by CRON AI LTD., London (GB)
Filed on Sep. 13, 2022, as Appl. No. 17/931,751.
Prior Publication US 2024/0087302 A1, Mar. 14, 2024
Int. Cl. G06V 10/80 (2022.01); G06V 10/25 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/803 (2022.01) [G06V 10/25 (2022.01); G06V 10/82 (2022.01); G06V 2201/07 (2022.01)] 19 Claims
OG exemplary drawing
 
1. A method for updating a cartesian representation of a region of interest (ROI), the method comprising:
receiving, by at least one sensor encoding neural network implemented in a computing device A, a sensor point of view (POV) data, wherein the sensor POV data is based on a sensor data generated by at least one sensor for a plurality of view cones;
generating, via the at least one sensor encoding neural network, a polar feature vector based on the sensor POV data, wherein the polar feature vector comprises a plurality of cone vectors corresponding to the plurality of view cones;
mapping, by a projection module implemented in a computing device B, at least one cell from a plurality of cells of the cartesian representation with at least one cone vector from the plurality of cone vectors, wherein the at least one cone vector corresponds to at least one view cone from the plurality of view cones;
generating, via an offset encoding module implemented in the computing device B, at least one offset vector based on one or more offset parameters corresponding to the at least one cell, wherein the at least one offset vector is generated further based on one or more sensor performance parameters, and wherein the one or more sensor performance parameters are indicative of a performance of the at least one sensor;
receiving, by the projection module, the at least one offset vector corresponding to the at least one cell, wherein the at least one offset vector is based on a position of the at least one sensor relative to the at least one cell of the cartesian representation;
concatenating, via the projection module, at least the at least one cone vector and the at least one offset vector to generate at least one transformed tensor corresponding to the at least one cell;
generating, via a convolutional neural network implemented in the computing device B, at least one learned output based on the at least one transformed tensor; and
generating, via an object detection network implemented in the computing device B, an updated cartesian representation based on the at least one learned output.