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公开(公告)号：US20180165546A1
公开(公告)日：2018-06-14
申请号：US15840868
申请日：2017-12-13
Applicant: Axis AB
Inventor： Markus Skans , Niclas Danielsson
CPC classification number: G06K9/6255 , G06K9/6234 , G06K9/6256 , G06K9/6262 , G06N3/0454 , G06N3/08 , G06N3/084
Abstract: A method, device and computer program product for training neural networks being adapted to process image data and output a vector of values forming a feature vector for the processed image data. The training is performed using feature vectors from a reference neural network as ground truth. A system of devices for tracking an object using feature vectors outputted by neural networks running on the devices.
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公开(公告)号：US20170083332A1
公开(公告)日：2017-03-23
申请号：US14859041
申请日：2015-09-18
Applicant: Axis AB
Inventor： Niclas Danielsson , Mikael Asker , Hans-Peter Nilsson , Markus Skans , Mikael Pendse
IPC: G06F9/30
CPC classification number: G06F9/30058 , G06T1/20
Abstract: A method and system are disclosed. The method may include receiving instructions in a hardware accelerator coupled to a computing device. The instructions may describe operations and data dependencies between the operations. The operations and the data dependencies may be predetermined. The method may include performing a splitter operation in the hardware accelerator, performing an operation in each of a plurality of branches, and performing a combiner operation in the hardware accelerator.
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公开(公告)号：US20160165257A1
公开(公告)日：2016-06-09
申请号：US14952051
申请日：2015-11-25
Applicant: AXIS AB
Inventor： Jiandan Chen , Markus Skans , Willie Betschart , Mikael Pendse , Alexandre Martins
IPC: H04N19/503 , H04N19/172 , H04N19/177
CPC classification number: H04N19/503 , H04N19/119 , H04N19/124 , H04N19/137 , H04N19/167 , H04N19/17 , H04N19/172 , H04N19/177 , H04N19/61 , H04N19/85
Abstract: A method and encoder for video encoding a sequence of frames is provided. The method comprises: receiving a sequence of frames depicting a moving object, predicting a movement of the moving object in the sequence of frames between a first time point and a second time point; defining, on basis of the predicted movement of the moving object, a region of interest (ROI) in the frames which covers the moving object during its entire predicted movement between the first time point and the second time point; and encoding a first frame, corresponding to the first time point, in the ROI and one or more intermediate frames, corresponding to time points being intermediate to the first and the second time point, in at least a subset of the ROI using a common encoding quality pattern defining which encoding quality to use in which portion of the ROI.
Abstract translation: 提供了一种用于帧序列的视频编码的方法和编码器。 该方法包括：接收描绘移动物体的一系列帧，预测在第一时间点和第二时间点之间的帧序列中的移动物体的移动; 基于所述移动物体的预测的移动，在所述第一时间点和所述第二时间点之间的整个预测移动期间，覆盖所述移动物体的所述帧中的感兴趣区域（ROI）; 并且在ROI的至少一个子集中使用公共编码对与ROI对应的第一帧和对应于第一和第二时间点之间的时间点的一个或多个中间帧编码对应于第一时间点的第一帧 质量模式定义了在ROI的哪个部分中使用的编码质量。
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公开(公告)号：US12094236B2
公开(公告)日：2024-09-17
申请号：US17038528
申请日：2020-09-30
Applicant: Axis AB
Inventor： Markus Skans , Christian Colliander , Martin Ljungqvist , Willie Betschart , Niclas Danielsson
IPC: G06V10/80 , G06F18/214 , G06F18/22 , G06N3/045 , G06T5/50 , G06V10/44 , G06V10/764 , G06V10/82 , G06V20/52 , G06V40/00 , G06V40/10 , G06V40/20
CPC classification number: G06V40/107 , G06F18/214 , G06F18/22 , G06N3/045 , G06T5/50 , G06V10/44 , G06V10/764 , G06V10/82 , G06V20/52 , G06V40/23
Abstract: A method of object re-identification in images of objects comprises providing a plurality of neural networks for object re-identification, wherein each of the plurality of neural networks is trained on image data with different sets of anatomical features, each set being represented by a reference vector; receiving a plurality of images of objects and an input vector representing anatomical features that are depicted in all of the plurality of images; comparing the input vector with the reference vectors for determining, according to a predefined condition, the most similar reference vector; and inputting image data of the plurality of objects to the neural network represented by the most similar reference vector for determining whether the plurality of objects have the same identity.
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Patent Agency Ranking
公开(公告)号：US20230360360A1
公开(公告)日：2023-11-09
申请号：US18312126
申请日：2023-05-04
Applicant: Axis AB
Inventor： Anton ÖHRN , Markus Skans , Niclas Danielsson
IPC: G06V10/44
CPC classification number: G06V10/443
Abstract: The present disclosure generally relates to a method for weighting of features in a feature vector of an object detected in a video stream capturing a scene, comprising: determining a feature vector comprising a set of features for a detected object in the video stream; acquiring a reference feature vector of a reference model of the scene; and assigning a weight to at least one feature of the determined feature vector, wherein the weight for a feature of the determined feature vector depends on a deviation measure indicative of a degree of deviation of the feature from a corresponding feature of the acquired reference feature vector of the reference model.
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公开(公告)号：US10956781B2
公开(公告)日：2021-03-23
申请号：US15840868
申请日：2017-12-13
Applicant: Axis AB
Inventor： Markus Skans , Niclas Danielsson
Abstract: A method, device and computer program product for training neural networks being adapted to process image data and output a vector of values forming a feature vector for the processed image data. The training is performed using feature vectors from a reference neural network as ground truth. A system of devices for tracking an object using feature vectors outputted by neural networks running on the devices.
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公开(公告)号：US10671887B2
公开(公告)日：2020-06-02
申请号：US15831638
申请日：2017-12-05
Applicant: Axis AB
Inventor： Niclas Danielsson , Markus Skans
Abstract: Methods and apparatus, including computer program products, for creating a quality annotated training data set of images for training a quality estimating neural network. A set of images depicting a same object is received. The images in the set of images have varying image quality. A probe image whose quality is to be estimated is selected from the set of images. A gallery of images is selected from the set of images. The gallery of images does not include the probe image. The probe image is compared to each image in the gallery and a match score is generated for each image comparison. Based on the match scores, a quality value is determined for the probe image. The probe image and its associated quality value are added to a quality annotated training data set for the neural network.
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公开(公告)号：US09990199B2
公开(公告)日：2018-06-05
申请号：US14859041
申请日：2015-09-18
Applicant: Axis AB
Inventor： Niclas Danielsson , Mikael Asker , Hans-Peter Nilsson , Markus Skans , Mikael Pendse
CPC classification number: G06F9/30058 , G06T1/20
Abstract: A method and system are disclosed. The method may include receiving instructions in a hardware accelerator coupled to a computing device. The instructions may describe operations and data dependencies between the operations. The operations and the data dependencies may be predetermined. The method may include performing a splitter operation in the hardware accelerator, performing an operation in each of a plurality of branches, and performing a combiner operation in the hardware accelerator.
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公开(公告)号：US09536154B2
公开(公告)日：2017-01-03
申请号：US14273181
申请日：2014-05-08
Applicant: Axis AB
Inventor： Markus Skans , Anders Johannesson , Jiandan Chen , Joakim Baltsen
IPC: G06K9/00 , G08B13/196 , G06K9/20 , G06K9/62 , G06T7/20
CPC classification number: G06K9/00771 , G06K9/00369 , G06K9/2036 , G06K9/2054 , G06K9/6201 , G06T7/292 , G06T2207/10016 , G06T2207/20112 , G06T2207/30196 , G06T2207/30232 , G08B13/19652 , G08B13/19686
Abstract: A method of monitoring a scene by a camera (7) comprises marking a part (14) of the scene with light having a predefined spectral content and a spatial verification pattern. An analysis image is captured of the scene by a sensor sensitive to the predefined spectral content. The analysis image is segmented based on the predefined spectral content, to find a candidate image region. A spatial pattern is detected in the candidate image region, and a characteristic of the detected spatial pattern is compared to a corresponding characteristic of the spatial verification pattern. If the characteristics match, the candidate image region is identified as a verified image region corresponding to the marked part (14) of the scene. Image data representing the scene is obtained, and image data corresponding to the verified image region is processed in a first manner, and remaining image data is processed in a second manner.
Abstract translation: 通过照相机（7）监视场景的方法包括用具有预定义频谱内容和空间验证模式的光标记场景的一部分（14）。 通过对预定义的光谱内容敏感的传感器捕获场景的分析图像。 分析图像基于预定义的光谱内容被分割，以找到候选图像区域。 在候选图像区域中检测空间图案，并将检测到的空间图案的特性与空间验证图案的相应特征进行比较。 如果特征匹配，则候选图像区域被识别为对应于场景的标记部分（14）的验证图像区域。 获得表示场景的图像数据，并且以第一方式处理与经验证的图像区域相对应的图像数据，并且以第二方式处理剩余的图像数据。
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