公开(公告)号：US12131518B2

公开(公告)日：2024-10-29

申请号：US17539261

申请日：2021-12-01

Applicant: Axis AB

Inventor： Niclas Danielsson ,  Haochen Liu

IPC: G06V10/74 ,  G06N3/04 ,  G06V10/44 ,  G06V10/762 ,  G06V10/82

CPC classification number: G06V10/761 ,  G06N3/04 ,  G06V10/44 ,  G06V10/762 ,  G06V10/82

Abstract: A method and a device associate an object detection in a first frame with an object detection in a second frame using a convolutional neural (CNN) network trained to determine feature vectors such that object detections relating to separate objects are arranged in separate clusters. The CNN determines a reference set of feature vectors associated with the object detection in the first frame, and candidate sets of feature vectors associated with a respective one of identified areas corresponding to object detections in the second frame. A set of closest feature vectors is determined, and then measure of closeness to the reference set of feature vectors is determined for each candidate. A respective weight is determined for each object detection in the second frame. The object detection in the first frame is associated with one of the object detections in the second frame based on the assigned weights.

公开(公告)号：US20230343082A1

公开(公告)日：2023-10-26

申请号：US18301543

申请日：2023-04-17

Applicant: Axis AB

Inventor： Haochen Liu ,  Jiandan Chen

IPC: G06V10/82 ,  G06V10/774 ,  G06V10/764

CPC classification number: G06V10/82 ,  G06V10/774 ,  G06V10/764

Abstract: A method for encoding training data for training of a neural network comprises: obtaining training data including multiple datasets, each dataset comprises images annotated with at least one respective object class, forming , each dataset having an individual background class associated with the object class; encoding the images of the datasets to be associated with their respective individual background class; encoding image patches belonging to annotated object classes to be associated with their respective object class; encoding each of the datasets, to include an ignore attribute (“ignore”) to object classes that are annotated only in the other datasets and to background classes formed for the other datasets of the multiple datasets, the ignore attribute indicating that the assigned object class and background classes do not contribute in adapting the neural network in training using the respective dataset; and providing the encoded training data for training of a neural network.