Axis Ab

Telefonaktiebolaget Lm Ericsson (publ)

Niclas Danielsson

Song Yuan

Markus Skans

Xing Danielsson Fan

Simon Molin

Axel Keskikangas

Anton Öhrn

Christian Colliander

Elias Jonsson

Thomas Winzell

Anders Hansson

Jesper Bengtsson

Håkan Ardö

Jiandan Chen

Aras Papadelis

Emanuel Johansson

Mikael Pendse

Martin Ljungqvist

Anders Lloyd

Hans-Peter Nilsson

Jakob Grundström

Anton Jakobsson

Willie Betschart

Haochen Liu

Mikael Asker

Amanda Nilsson

Sarah Laross

Fan Xing Danielsson

A plurality of feature vectors for moveable objects are received in a sequence of image frames captured during a first period. The feature vectors are received from a machine learning module trained to extract similar feature vectors in different image frames. An initial value is assigned to a first feature vector , or to a cluster of feature vectors identified in a second sequence of image frames preceding the first image frame. The indicator indicates whether vector is alive. For each subsequent image frame , the indicator is updated by: updating the value based on a similarity between the feature vector and the first feature vector or the cluster of feature vectors. If the value of the indicator indicates that the vector is alive, determining that there is a moveable object that is located in the captured scene at least the predetermined portion of the given period of time.

Device and method for determining whether there is a moveable object located in a scene at least a predetermined portion of a given period of time

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.

Object re-identification in video streams

A device, a non-transitory computer-readable storage medium, and a method of signing a metadata frame corresponding to an image frame of a sequence of image frames are disclosed. The metadata frame comprises metadata of one or more detected objects in the image frame, and the metadata of each detected object comprises coordinates defining a location in the image frame of the detected object. A digital signature is generated based on at least a subset of the metadata in the metadata frame, and additional metadata are added to the metadata frame. The additional metadata comprise the digital signature and predefined coordinates which define that the additional metadata comprise the digital signature. Furthermore, a device, a non-transitory computer-readable storage medium, and method of authenticating a digitally signed metadata frame corresponding to an image frame of a sequence of image frames are disclosed.

Device and a method for signing a metadata frame corresponding to an image frame of a sequence of image frames

A method of transmitting an image stream together with color metadata is disclosed. The method comprises capturing image frames of a first image stream, capturing image frames of a second image stream, using camera settings for prioritizing color capture, detecting an object in a first image frame of the first image stream and determining color information associated with the object based on the second image stream. The method further comprises adding the color information as color metadata to the first image stream, encoding the first image stream, and transmitting the encoded first image stream together with the color metadata. A corresponding device is also disclosed.

Image stream with color metadata

A method for prioritizing feature extraction for object re-identification in an object tracking application. Region of interests (ROI) for object feature extraction is determined based on motion areas in the image frame. Each object detected in an image frame and which is at least partly overlapping with a ROI is associated with the ROI. A list of candidate objects for feature extraction is determined by, for each ROI associated with two or more objects: adding each object of the two or more objects that is not overlapping with any of the other objects among the two or more objects with more than a threshold amount. From the list of candidate objects, at least one object is selected, and image data of the image frame depicting the selected object is used for determining a feature vector for the selected object.

Method, system and non-transitory computer-readable media for prioritizing objects for feature extraction

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.

Device and method for enhancing tracking of objects in a scene captured in a video sequence

There are provided encoding and decoding methods, and corresponding systems which are beneficial in connection to performing a search among regions of interest, ROIs, in encoded video data. In the encoded video data, there are independently decodable ROIs. These ROIs and the encoded video frames in which they are present are identified in metadata which is searched responsive to a search query. The encoded video data further embeds information which associates the ROIs with sets of coding units, CUs, that spatially overlap with the ROIs. In connection to independently decoding the ROIs found in the search, the embedded information is used to identify the sets of CUs to decode.

Methods and systems for encoding and decoding of video data in connection to performing a search in the video data

A method of generating a segmentation outcome which indicates individual instances of one or more object classes for an image in a sequence of images is disclosed. The method comprises: determining () a coherent region of the image; processing () the image to determine a tensor representing pixel-specific confidence scores; generating () a series of temporary segmentation masks for the coherent region, wherein each temporary segmentation mask is generated by interpreting the tensor with respect to a single object class using a different temporary confidence score threshold; evaluating () the series of temporary segmentation masks to determine if an object mask condition is met; depending on the outcome of the evaluation, setting () the temporary confidence score threshold as a final confidence score threshold for the pixels of the temporary segmentation mask, or setting () a default confidence score threshold as a final confidence score threshold for the coherent region; and generating () a final segmentation outcome for the image.

Segmentation method

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.

Device and a method for associating object detections between frames using a neural network

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.

Method of object re-identification

Methods, systems and computer program products, for producing streams of image frames. Image frames in streaming video are segmented into background segments and instance segments. A background image frame containing the background segments is created. At least some of the instance segments are classified into movable objects of interest and movable objects of non-interest. During a background update time period, the background image frame is updated when a movable object of non-interest has moved to reveal a background area, to include the revealed background area in the background image frame. A foreground image containing the movable objects of interest is created. Blocks of pixels of the updated background and foreground image frames are encoded. A stream of encoded foreground image frames having a first frame rate is produced. A stream of encoded updated background image frames a second, lower frame rate is produced.

Method and system for producing streams of image frames

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.

Method and controller for controlling a video processing unit to facilitate detection of newcomers in a first environment

In a method for tracking an object in video-monitoring scenes, multiple feature vectors are extracted () and assembled () 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 (). 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 () with the second object.

Method and device for tracking an object

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.

Method, computer program product and device for training a neural network

A method and system for tracking objects in a defined area compares image data of a detected object to profiles of persons that have entered the defined area to find the best match and connect the profile of the best match to the detected object. Identification profiles of persons that have been identified, by presenting their credentials, when entering the defined area are registered as candidates and are later matched with objects detected in the defined area. The system and method use the physical access control system of the defined area to reduce the number of candidates for the detected objects to the most likely candidates. The processing time and need for resources of the object tracking in the defined area are thereby reduced.

Growing community of inventors

Lund, Sweden

Niclas Danielsson