Amazon Technologies, Inc.

Lelin Zhang

Amit Kumar Agrawal

Qiang Liu

Rohith Mysore Vijaya Kumar

Visesh Uday Kumar Chari

Abhishek Singh

Mashhour Solh

Jinglun Gao

David Ting-Yu Wu

Prakash Ramu

Yadunandana Nagaraja Rao

Weijian Wang

A system configured to improve human presence detection and/or localization by generating aggregate confidence values. The system may aggregate confidence values corresponding to overlapping regions of interest. The system may perform human presence detection by comparing the aggregate confidence values to a universal threshold, with aggregate confidence values above the universal threshold indicating that human presence is detected. The system may use the aggregate confidence values to generate a heatmap, may identify a strongest cluster of pixels in the heatmap and determine a bounding box surrounding the strongest cluster. To distinguish the strongest cluster from a second strongest cluster of pixels, the system may apply a Gaussian kernel with varying threshold values. The system may store the threshold value at which the strongest cluster separates from the second strongest cluster and may use the threshold value to generate the bounding box.

Presence detection and detection localization

Devices and techniques are generally described for segmentation of object image data from background image data. In some examples, the segmentation of object image data may comprise capturing image data comprising color data and depth data. In some examples, the segmentation of object image data may further include separating the depth data into a plurality of clusters of image data, wherein each cluster is associated with a respective range of depth values. In various examples, the segmentation of object image data may comprise selecting a main cluster of image data as corresponding to an object of interest in the image data. In various other examples, the segmentation of object image data may comprise identifying pixels of the main cluster that correspond to the object of interest.

Segmentation of object image data from background image data

A system configured to improve human presence detection and/or localization by generating aggregate confidence values. The system may aggregate confidence values corresponding to overlapping regions of interest. The system may perform human presence detection by comparing the aggregate confidence values to a universal threshold, with aggregate confidence values above the universal threshold indicating that human presence is detected. The system may use the aggregate confidence values to generate a heatmap, may identify a strongest cluster of pixels in the heatmap and determine a bounding box surrounding the strongest cluster. To reduce false positive detections, the system may track the false positive detections as heatmap base data and may remove the heatmap base data from the heatmap. Thus, pixel intensity values corresponding to the false positive detections may be reduced to improve an accuracy of the human presence detection.

Devices and techniques are generally described for segmentation of image data using depth data. In various examples, color image data may be received from a digital camera. In some examples, depth image data may be received from a depth sensor. In various examples, the depth image data may be separated into a plurality of clusters of depth image data, wherein each cluster is associated with a respective range of depth values. In some further examples, a determination may be made that a first cluster of image data corresponds to an object of interest, such as a human subject, in the image data. In various examples, pixels of the first cluster may be encoded with foreground indicator data. In some further examples, segmented image data may be generated. The segmented image data may comprise pixels encoded with the foreground indicator data.

Image data segmentation using depth data

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Lelin Zhang