A computer-implemented method for prioritizing objects for feature extraction for the purpose of object re-identification in an object tracking application, comprising the steps of: receiving (S602) an image frame (100a-e) depicting a scene comprising a plurality of objects; receiving (S604) object detection data comprising, for each object of the plurality of objects , localization data indicating location and spatial extent of the object in the image frame; receiving (S606) motion data indicating one or more motion areas in the image frame, each motion area corresponding to an area in the scene where motion has been detected, wherein the motion data further comprises, for each motion area in the image frame, an indication of a velocity of the detected motion in the corresponding area of the scene; for each motion area, determining (S608) a region of interest, ROI, for object feature extraction in the image frame, the ROI for object feature extraction overlapping with the motion area, and extending (S610) the ROI based on the velocity; for each ROI for object feature extraction, determining a list of objects at least partly overlapping with the ROI for object feature extraction using the object detection data, and associating (S612) the ROI for object feature extraction with the list of objects; determining (S614) a list of prioritized candidate objects for feature extraction by, for each ROI for object feature extraction associated (S704) with two or more objects: for each object of the two or more objects, upon determining (S708) that the object is not overlapping with any of the other objects of the plurality of objects with more than a threshold amount, adding (S710) the object to the list of prioritized candidate objects for feature extraction; selecting (S616) at least one object among the list of prioritized candidate objects, and for each selected object determining (S618) a feature vector for the selected object based on image data of the image frame according to the localization data of the object. receiving (S602) an image frame (100a-e) depicting a scene comprising a plurality of objects; receiving (S604) object detection data comprising, for each object of the plurality of objects , localization data indicating location and spatial extent of the object in the image frame; receiving (S606) motion data indicating one or more motion areas in the image frame, each motion area corresponding to an area in the scene where motion has been detected, wherein the motion data further comprises, for each motion area in the image frame, an indication of a velocity of the detected motion in the corresponding area of the scene; for each motion area, determining (S608) a region of interest, ROI, for object feature extraction in the image frame, the ROI for object feature extraction overlapping with the motion area, and extending (S610) the ROI based on the velocity; for each ROI for object feature extraction, determining a list of objects at least partly overlapping with the ROI for object feature extraction using the object detection data, and associating (S612) the ROI for object feature extraction with the list of objects; determining (S614) a list of prioritized candidate objects for feature extraction by, for each ROI for object feature extraction associated (S704) with two or more objects: for each object of the two or more objects, upon determining (S708) that the object is not overlapping with any of the other objects of the plurality of objects with more than a threshold amount, adding (S710) the object to the list of prioritized candidate objects for feature extraction; selecting (S616) at least one object among the list of prioritized candidate objects, and for each selected object determining (S618) a feature vector for the selected object based on image data of the image frame according to the localization data of the object. <EOS>