Invention Grant
- Patent Title: Deep cross-correlation learning for object tracking
-
Application No.: US15708014Application Date: 2017-09-18
-
Publication No.: US11308350B2Publication Date: 2022-04-19
- Inventor: Amirhossein Habibian , Cornelis Gerardus Maria Snoek
- Applicant: QUALCOMM Incorporated
- Applicant Address: US CA San Diego
- Assignee: QUALCOMM Incorporated
- Current Assignee: QUALCOMM Incorporated
- Current Assignee Address: US CA San Diego
- Agency: Seyfarth Shaw LLP
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06N3/067 ; G06N3/08 ; G06V10/82 ; G06N3/04 ; G06V10/20 ; G06V10/44 ; G06V10/62

Abstract:
An artificial neural network for learning to track a target across a sequence of frames includes a representation network configured to extract a target region representation from a first frame and a search region representation from a subsequent frame. The artificial neural network also includes a cross-correlation layer configured to convolve the extracted target region representation with the extracted search region representation to determine a cross-correlation map. The artificial neural network further includes a loss layer configured to compare the cross-correlation map with a ground truth cross-correlation map to determine a loss value and to back propagate the loss value into the artificial neural network to update filter weights of the artificial neural network.
Public/Granted literature
- US20180129906A1 DEEP CROSS-CORRELATION LEARNING FOR OBJECT TRACKING Public/Granted day:2018-05-10
Information query