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Primary user selection for head tracking

專利號(hào)
US10027883B1
公開(kāi)日期
2018-07-17
申請(qǐng)人
Amazon Technologies, Inc.(US NV Reno)
發(fā)明人
Cheng-Hao Kuo; Jim Oommen Thomas; Tianyang Ma; Stephen Vincent Mangiat; Sisil Sanjeev Mehta; Ambrish Tyagi; Amit Kumar Agrawal; Kah Kuen Fu; Sharadh Ramaswamy
IPC分類
G06K9/00; G06K9/46; G06K9/66; H04N5/225; H04N5/232
技術(shù)領(lǐng)域
face,image,in,tracking,user,bounding,or,device,algorithm,can
地域: NV NV Reno

摘要

Various embodiments enable a primary user to be identified and tracked using stereo association and multiple tracking algorithms. For example, a face detection algorithm can be run on each image captured by a respective camera independently. Stereo association can be performed to match faces between cameras. If the faces are matched and a primary user is determined, a face pair is created and used as the first data point in memory for initializing object tracking. Further, features of a user's face can be extracted and the change in position of these features between images can determine what tracking method will be used for that particular frame.

說(shuō)明書

Further, in tracking mode 206, the tracking algorithm returns information for a bounding box containing a respective representation of a face and features of a user's face are extracted from within the bounding box for each image to determine an output for the location of a user's eyes, mouth, or one or more points relative to these features, for example, that is smoother and reduces jitteriness relative to simply providing the current location of these features. The change in position of these features between subsequent images can be used to determine what output or to adjust an output for a current location of these features. For example, the change in position of optical flow of a user's eyes can be calculated for a current and previous image. If this change in position is less than a first threshold, then the position of the user has only slightly changed relative to their position in the previous frame. Since this change is small, the user's current eye position can be reasonable estimated as their location in the previous frame, as if the user hasn't moved. In another example, if this change is between the first threshold and a second threshold, a single point tracking algorithm can be used to track the user's eyes between these two frames. If, however, this change in optical flow is greater than the second threshold, the current position of the user's eyes can be used. In this instance, the tracking output will appear quite jittery, however, since the change in eye position is so great (i.e., greater than the second threshold) the user has moved quickly or abruptly and, thus, an abrupt change, in this instance, would not only be acceptable, it would likely be expected. Once the current location of the eyes, in this example, is determined for each image captured by each camera, stereo disparity between this current location between these images is determined. The stereo disparity is then used to determine a z-depth for the eyes, by calculating a distance between the eyes and the computing device, in order to determine a three-dimensional position (x, y, z) of the eyes relative to the computing device.

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