Axis Ab

Anton Öhrn

Song Yuan

Niclas Danielsson

Markus Skans

Björn Benderius

Viktor Edpalm

Xing Danielsson Fan

Fredrik Pihl

Jimmie Jönsson

Johan Jeppsson

Niclas Svensson

Sebastian Fors

Henrik Eliasson

Andreas Muhrbeck

Amanda Nilsson

Karin Dammer

Johan Sternby

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 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

A method and a digital video camera for reducing intensity variations in a video stream depicting a scene comprising capturing, using a first sensor setting, a first frame; detecting a change in intensity in a portion of the first frame, the portion represents a first area of the scene; determining a second sensor setting based on the first frame; capturing, using the second sensor setting, a second frame; creating a local tone mapping mask, wherein a local tone mapping in the first area of the scene is different from a local tone mapping in an area outside the first area, and wherein the local tone mapping in the area outside the first area is based on a relation between the first sensor setting and the second sensor setting; and applying the local tone mapping mask to the second frame.

Method for reducing intensity variations in a video image stream depicting a scene

A method of encoding a video stream comprising receiving a frame of a video stream to be encoded, performing motion detection in the frame to identify steady regions, and temporally filtering the frame so as to provide temporally filtered pixel values. For a pixel in the steady regions of the frame, the temporally filtered pixel value is used when encoding the pixel, on a condition that the pixel belongs to a block of pixels of the frame that will be intra-coded. On a condition that the pixel instead belongs to a block of pixels of the frame that will be inter-coded, a value that was used when encoding a corresponding pixel of the immediately preceding frame is used when encoding the pixel.

Method, a video encoder, and a video camera for encoding a video stream

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Lund, Sweden

Anton Öhrn