Apple Inc.

Benjamin M Olson

Jianping Zhou

Ralph T Brunner

Suk Hwan Lim

Rudolph Van Der Merwe

Todd Stephen Sachs

Frank Doepke

Marco Zuliani

Claus Molgaard

Sebastien X Beysserie

George E Williams

Geoffrey T Anneheim

Xiaoxing Li

Ethan J Tira-Thompson

Anita Nariani-Schulze

Systems, methods, and computer readable media to rapidly identify and track an arbitrary sized object through a temporal sequence of frames is described. The object being tracked may initially be identified via a specified or otherwise known region-of-interest (ROI). A portion of that ROI can be used to generate an initial or reference histogram and luminosity measure, metrics that may be used to identify the ROI in a subsequent frame. For a frame subsequent to the initial or reference frame, a series of putative ROIs (each having its own location and size) may be identified and the 'best' of the identified ROIs selected. As used here, the term 'best' simply means that the more similar two frames' histograms and luminosity measures are, the better one is with respect to the other.

Fast histogram-based object tracking

Techniques to detect subject and camera motion in a set of consecutively captured image frames are disclosed. More particularly, techniques disclosed herein temporally track two sets of downscaled images to detect motion. One set may contain higher resolution and the other set lower resolution of the same images. For each set, a coefficient of variation may be computed across the set of images for each sample in the downscaled image to detect motion and generate a change mask. The information in the change mask can be used for various applications, including determining how to capture a next image in the sequence.

Scene stability detection

A method to improve the efficiency of the detection and tracking of machine-readable objects is disclosed. The properties of image frames may be pre-evaluated to determine whether a machine-readable object, even if present in the image frames, would be likely to be detected. After it is determined that one or more image frames have properties that may enable the detection of a machine-readable object, image data may be evaluated to detect the machine-readable object. When a machine-readable object is detected, the location of the machine-readable object in a subsequent frame may be determined based on a translation metric between the image frame in which the object was identified and the subsequent frame rather than a detection of the object in the subsequent frame. The translation metric may be identified based on an evaluation of image data and/or motion sensor data associated with the image frames.

Efficient machine-readable object detection and tracking

Growing community of inventors

Sunnyvale, CA, United States of America

Benjamin M Olson