Amazon Technologies, Inc.

Che-Chun Su

Cheng-Hao Kuo

Oleg Rybakov

Jim Oommen Thomas

Vivek A Shah

Yelin Kim

Jingjing Zheng

Anuja Shantaram Dawane

Zhuo Deng

A system configured to reduce false positives when performing human presence detection is provided. In addition to calculating a Human Detection (HD) confidence score during human presence detection, the system may use human keypoint detection (HKD) techniques to calculate a true positive (TP) confidence score and detect false positives based on a combination of the two confidence scores. For example, the device system may generate keypoint data, which indicates a location and maximum confidence value for individual keypoints associated with a human body. The system may input the keypoint data to a model configured to generate the TP confidence score, such as a logistic regression model that is configured to receive numerical values as inputs (e.g., HD confidence score and 17 keypoint confidence values) and generate the TP confidence score. The system then detects false positives using the TP confidence score and may remove corresponding bounding boxes.

False positive suppression using keypoints

An autonomous mobile device (AMD) or other device may perform various tasks during operation. The AMD includes a camera to acquire an image. Some tasks, such as presenting information on a display screen or a video call, may involve the AMD determining whether a user is engaged with the AMD. The AMD may move a component, such as the camera or the display screen, to provide a best experience for an engaged user. Images from the camera are processed to determine attributes of the user, such as yaw of the face of the user, pitch of the face of the user, distance from the camera, and so forth. Based on the values of these attributes, a user engagement score is determined. The score may be used to select a particular user from many users in the image, or to otherwise facilitate operation of the AMD.

System to determine user engagement with autonomous mobile device

A plurality of instances of image data can be analyzed, and favored aspects of each instance identified and utilized in generating an enhanced output image. For example, a plurality of instances of image data can be analyzed to identify metric values associated with each pixel location, such as contrast, saturation, and exposedness. A weight map corresponding to each metric is generated for each instance of image data, each weight map indicating a value for the metric at each pixel location of the instance of image data. The weight maps associated with each instance of image data are merged, and a Gaussian pyramid of the merged weight map for each instance of image data is determined along with a Laplacian pyramid for each instance of image data. The Gaussian pyramids and Laplacian pyramids are merged into a Laplacian pyramid, which is then collapsed to form an enhanced output image.

Enhanced image generation based on multiple images

Growing community of inventors

Renton, WA, United States of America

Che-Chun Su