公开(公告)号：US20190228272A1

公开(公告)日：2019-07-25

申请号：US15878188

申请日：2018-01-23

Applicant: Intelligent Fusion Technology, Inc

Inventor： Dan SHEN ,  Peter ZULCH ,  Marcello DISASIO ,  Erik BLASCH ,  Genshe CHEN ,  Zhonghai WANG ,  Jingyang LU

IPC: G06K9/62 ,  G06N7/08 ,  G06K9/68 ,  G06T7/80 ,  G01S13/58 ,  H04N5/33

Abstract: The present disclosure provides a method for joint manifold learning based heterogenous sensor data fusion, comprising: obtaining learning heterogeneous sensor data from a plurality sensors to form a joint manifold, wherein the plurality sensors include different types of sensors that detect different characteristics of targeting objects; performing, using a hardware processor, a plurality of manifold learning algorithms to process the joint manifold to obtain raw manifold learning results, wherein a dimension of the manifold learning results is less than a dimension of the joint manifold; processing the raw manifold learning results to obtain intrinsic parameters of the targeting objects; evaluating the multiple manifold learning algorithms based on the raw manifold learning results and the intrinsic parameters to determine one or more optimum manifold learning algorithms; and applying the one or more optimum manifold learning algorithms to fuse heterogeneous sensor data generated by the plurality sensors.

公开(公告)号：US20220172122A1

公开(公告)日：2022-06-02

申请号：US17563014

申请日：2021-12-27

Applicant: Intelligent Fusion Technology, Inc.

Inventor： Dan SHEN ,  Peter ZULCH ,  Marcello DISASIO ,  Erik BLASCH ,  Genshe CHEN

IPC: G06N20/00 ,  H04N5/33 ,  G06K9/62

Abstract: The present disclosure provide a system, a method, and a storage medium for distributed joint manifold learning (DJML) based heterogeneous sensor data fusion. The system includes a plurality of nodes; and each node includes at least one camera; one or more sensors; at least one memory configured to store program instructions; and at least one processor, when executing the program instructions, configured to obtain heterogeneous sensor data from the one or more sensors to form a joint manifold; determine one or more optimum manifold learning algorithms by evaluating a plurality of manifold learning algorithms based on the joint manifold; compute a contribution of the node based on the one or more optimum manifold learning algorithms; update a contribution table based on the contribution of the node and contributions received from one or more neighboring nodes; and broadcast the updated contribution table to the one or more neighboring nodes.