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The talk starts with a description of the GE Sherlock system which encompasses methods such as person tracking in crowds, dynamic PTZ camera control, facial analytics from a distance such as gaze estimation and expression recognition, upper body affective pose analysis and the inference of social states such as rapport and hostility.
A review paper [21] provided an overview of usage of first-generation Kinect 360 sensor for human activity analysis, including body pose and activity recognition, and hand gesture analysis; however, they do not include any references on using head gaze information.
We formulate the body pose estimation as an analysis-by-synthesis optimisation algorithm, where a generic 3D human body model is used to illustrate the pose and the silhouettes extracted from the images are used as constraints.
In this paper, we address the rigid body pose stabilization problem using dual quaternion formalism.
In this paper we address the problem of human body pose estimation from still images.
The availability of body pose and, especially, body movement information has been found to increase action classification performance [8].
For example, Google has demonstrated success in detecting human body pose in images.
Body pose is used for hands search and left/right hand assignment.
We then compute the joint probability of true body pose and true gaze: (9).
These two different types of features correspond to body pose and human-object interaction, respectively.
Figure 2 Body pose estimation using the Kinect 3D sensor to extract hand locations.
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