Intel Corporation

Parual Datta

Omesh Tickoo

Srenivas Varadarajan

Andradige Pubudu Madhawa Silva

Nikita Tiwari

Erin Carroll

Javier Felip Leon

Nilesh Amar Ahuja

Estimating a head pose may include obtaining sensor data corresponding to a head and at least a portion of the body of a human subject and determining an estimate of a three-dimensional (3D) body pose using the obtained sensor data. The estimation can further include generating a first rendering of at least the human subject's head using the obtained sensor data and generating a plurality of head pose sample data sets by applying the estimated 3D body pose to a head-pose generative model. Further, the head pose estimation can include generating a plurality of second renderings respectively from each of the plurality of head pose sample data sets; determining which of the plurality of second renderings is closest to the first rendering; and selecting the second rendering determined to be closest to the first rendering.

Systems, methods, and devices for head pose determination

A mechanism is described for facilitating age classification of humans using image depth and human pose according to one embodiment. A method of embodiments, as described herein, includes facilitating, by one or more cameras of a computing device, capturing of a video stream of a scene having persons, and computing overall-depth torso lengths of the persons based on depth torso lengths of the persons. The method may further include comparing the overall-depth torso lengths with a predetermined threshold value representing a separation age between adults and children, and classifying a first set of the persons as adults if a first set of the overall-depth torso lengths associated with the first set of persons is greater than the threshold value.

Age classification of humans based on image depth and human pose

An example apparatus for estimating poses includes a person estimator to estimate a number of people based on a significant head count of received refined part detections. The apparatus includes a detection clusterer to cluster the refined part detections based on the estimated number of people to generate clustered part detections. The apparatus includes a candidate selector to select candidate person clusters for each clustered part detection based on proximity to the clustered part detection. The apparatus includes a sequential assigner to calculate a cluster affinity score for each combination of candidate person cluster and clustered part detection, and greedily sequentially assign each clustered part detection to a candidate person cluster based on the cluster affinity score to generate person clusters. The apparatus includes a pose generator to generate a pose for each person cluster.

Estimating multi-person poses using greedy part assignment

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Bengaluru, India

Parual Datta