Axis Ab

Jakob Grundström

Simon Molin

Niclas Danielsson

Markus Skans

Hanna Björgvinsdóttir

Martin Ljungqvist

Christian Colliander

Joakim Baltsén

A method for determining images plausible to have a false negative object detection comprises providing a group of historic trajectories, wherein each historic trajectory comprises a reference track that represents one or more historic tracks and comprises an object class of historic object detections that belong to the one or more historic tracks; performing tracking; performing object detection; for a determined track that does not match any determined object detection, comparing the determined track with reference tracks of historic trajectories for identifying a matching reference track; upon identifying a matching reference track, defining images of the determined track as being plausible to have a false negative object detection for the object class of the historic trajectory comprising the matching reference track; and upon not identifying a matching reference track, defining the determined track as a false positive track.

Method for determining images plausible to have a false negative object detection

A method, system, and apparatus related to the field of background subtraction in images is disclosed. In particular, the method, system, and apparatus is related to determining whether pixel positions in an image frame of a video sequence belongs to a background or a foreground of a captured scene using a determined level of dynamics of the pixel position.

Method, device and system for determining whether pixel positions in an image frame belong to a background or a foreground

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.

Method for locating one or more candidate digital images being likely candidates for depicting an object

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Lund, Sweden

Jakob Grundström