公开(公告)号：US20250133215A1

公开(公告)日：2025-04-24

申请号：US18901464

申请日：2024-09-30

Applicant: Axis AB

Inventor： Niclas Danielsson ,  Xing Danielsson Fan

IPC: H04N19/154 ,  H04N19/119 ,  H04N19/136 ,  H04N19/17 ,  H04N19/186 ,  H04N19/20 ,  H04N19/42

Abstract: A method of encoding images in a video, comprises: acquiring an original image from an image sensor of a video camera; encoding the original image using a generative image model, thereby obtaining a first encoded image; decoding the image to obtain a first decoded image; identify a region of interest (ROI) of the original image; for each ROI: perform an encoding quality check by comparing several reference points in the ROI of the original image against corresponding reference points in the ROI of the first decoded image, thereby obtaining a difference; if the difference is greater than a threshold, encoding the ROI using a non-generative image model, thereby obtaining a non-generative encoded image area; providing final encoded image data comprising a) the non-generative encoded image areas for the ROI having a difference greater than the threshold and b) the first encoded image for a remaining part of the original image.

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：US11024039B2

公开(公告)日：2021-06-01

申请号：US16686240

申请日：2019-11-18

Applicant: Axis AB

Inventor： Niclas Danielsson ,  Markus Skans

IPC: G06T7/246 ,  G06T7/292 ,  G06T7/73 ,  G06K9/00 ,  G06K9/62 ,  H04N5/247 ,  H04N7/18

Abstract: In a method for tracking an object in video-monitoring scenes, multiple feature vectors are extracted (722) and assembled (724) in point clouds, wherein a point cloud may be assembled for each tracklet, i.e. for each separate part of a track. In order to determine if different tracklets relate to the same or different objects the point clouds of each tracklet is compared (734). Based on the outcome of the comparison it is deduced if the first object and the second object may be considered to be the same object and, if so, the first object is associated (738) with the second object.

公开(公告)号：US20190188512A1

公开(公告)日：2019-06-20

申请号：US16208587

申请日：2018-12-04

Applicant: Axis AB

Inventor： Niclas Danielsson ,  Simon Molin ,  Markus Skans

IPC: G06K9/32 ,  G06N3/08 ,  G06T7/44 ,  G06T7/00

Abstract: A method and an image processing entity for applying a convolutional neural network to an image are disclosed. The image processing entity processes the image while using the convolutional kernel to render a feature map, whereby a second feature map size of the feature map is greater than a first feature map size of the feature maps with which the feature kernel was trained. Furthermore, the image processing entity repeatedly applies the feature kernel to the feature map in a stepwise manner, wherein the feature kernel was trained to identify the feature based on the feature maps of the first feature maps, wherein the feature kernel has the first feature map size.

公开(公告)号：US20190087687A1

公开(公告)日：2019-03-21

申请号：US16123773

申请日：2018-09-06

Applicant: Axis AB

Inventor： Niclas Danielsson ,  Simon Molin ,  Markus Skans ,  Jakob Grundström

IPC: G06K9/62 ,  G06N3/08 ,  G06F17/30

Abstract: A method for finding one or more candidate digital images being likely candidates for depicting a specific object comprising: receiving an object digital image depicting the specific object; determining, using a classification subnet of a convolutional neural network, a class for the specific object depicted in the object digital image; selecting, based on the determined class for the specific object depicted in the object digital image, a feature vector generating subnet from a plurality of feature vector generating subnets; determining, by the selected feature vector generating subnet, a feature vector of the specific object depicted in the object digital image; locating one or more candidate digital images being likely candidates for depicting the specific object depicted in the object digital image by comparing the determined feature vector and feature vectors registered in a database, wherein each registered feature vector is associated with a digital image.

公开(公告)号：US20170169306A1

公开(公告)日：2017-06-15

申请号：US14963792

申请日：2015-12-09

Applicant: Axis AB

Inventor： Jiandan Chen ,  Anders Lloyd ,  Niclas Danielsson

IPC: G06K9/46 ,  G06K9/62

CPC classification number: G06K9/4647 ,  G06K9/46 ,  G06K9/6202

Abstract: A method may include determining a value indicative of an average intensity of blocks in an image. The blocks include a primary and outer blocks. Each of the outer blocks may have three, five, or more than five pixels. The image may describe an external pixel lying between the primary and at least one of the outer blocks. The external pixel may not contribute to the value indicative of the average intensity of any of the blocks. The image may also describe a common internal pixel lying within two of the blocks. The common pixel may contribute to the value indicative of the average intensity of the two of the blocks. The method may include comparing the value indicative of the average intensity of the primary block to the values of the outer blocks, and quantifying a feature represented by the image by generating a characteristic number.

公开(公告)号：US12283057B2

公开(公告)日：2025-04-22

申请号：US18439200

申请日：2024-02-12

Applicant: Axis AB

Inventor： Niclas Danielsson ,  Markus Skans ,  Anton Öhrn

IPC: G06T7/246

Abstract: A method for selecting a crop score threshold for enhancing tracking of objects in a scene captured in a video sequence is disclosed. A respective track is obtained for two different objects, each track comprising crops of object instances of the objects in in a video sequence, each crop having a crop score and a feature vector. Each track is split into respective more tracklets thereby forming four or more tracklets. For each candidate crop score threshold a respective difference between each tracklet and each other tracklet is determined based on differences between feature vectors of crops having a crop score above the candidate crop score threshold of each tracklet, and each other tracklet. A crop score threshold is selected from the set of crop score thresholds resulting in a maximum difference between the differences between tracklets of different tracks and the differences between tracklets of the same track.

公开(公告)号：US20240420466A1

公开(公告)日：2024-12-19

申请号：US18732991

申请日：2024-06-04

Applicant: Axis AB

Inventor： Song YUAN ,  Anton Öhrn ,  Niclas Danielsson

IPC: G06V20/40 ,  G06V10/776 ,  G06V10/82 ,  G06V20/52

Abstract: The present system and method generally relate to the field of camera surveillance, and in particular to object re-identification in video streams captured by a camera.

公开(公告)号：US20240303828A1

公开(公告)日：2024-09-12

申请号：US18439200

申请日：2024-02-12

Applicant: Axis AB

Inventor： Niclas Danielsson ,  Markus Skans ,  Anton Öhrn

IPC: G06T7/246

CPC classification number: G06T7/246 ,  G06T2207/10016 ,  G06T2207/20021

Abstract: A method for selecting a crop score threshold for enhancing tracking of objects in a scene captured in a video sequence is disclosed. A respective track is obtained for two different objects, each track comprising crops of object instances of the objects in in a video sequence, each crop having a crop score and a feature vector. Each track is split into respective more tracklets thereby forming four or more tracklets. For each candidate crop score threshold a respective difference between each tracklet and each other tracklet is determined based on differences between feature vectors of crops having a crop score above the candidate crop score threshold of each tracklet, and each other tracklet. A crop score threshold is selected from the set of crop score thresholds resulting in a maximum difference between the differences between tracklets of different tracks and the differences between tracklets of the same track.

公开(公告)号：US11164008B2

公开(公告)日：2021-11-02

申请号：US16166955

申请日：2018-10-22

Applicant: Axis AB

Inventor： Xing Danielsson Fan ,  Niclas Danielsson ,  Anton Jakobsson ,  Emanuel Johansson ,  Thomas Winzell ,  Jesper Bengtsson

IPC: G06K9/00 ,  G08B13/196 ,  H04N7/18 ,  H04N5/33 ,  H04N19/167 ,  G07C9/00

Abstract: A method and a controller for controlling a video processing unit to facilitate detection of newcomers in a first environment. The method comprises: capturing a thermal image of a human object in the first environment, the first environment being associated with a first climate; calculating, based on the thermal image, a thermal signature of a portion of the human object; determining that the human object has entered the first environment from a second environment when the thermal signature of the portion of the human object deviates from a predetermined thermal signature associated with the first environment, wherein the second environment is associated with a second, different, climate; and controlling the video processing unit to prioritize the human object over other human objects when processing video frames depicting the human object together with the other human objects.