Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Techniques are provided for Head-Related Transfer Function (HRTF) measurement and sharing for audio applications. A methodology implementing the techniques according to an embodiment includes receiving a plurality of three-dimensional (3D)images of a user. The images are generated by a 3D camera or other image capture device, and each image is associated with a unique viewing angle or perspective of the user. The method also includes generating a 3D volume based on the 3D images and then generating a 3D mesh surface based on the 3D volume. The method further includes computing HRTFs associated with the user, based on the 3D mesh surface. Additionally, techniques are disclosed to provide a platform for sharing of the calculated HRTFs between audio applications executing on different devices.

Abstract: Techniques are provided for Head-Related Transfer Function (HRTF) measurement and sharing for audio applications. A methodology implementing the techniques according to an embodiment includes receiving a plurality of three-dimensional (3D) images of a user. The images are generated by a 3D camera or other image capture device, and each image is associated with a unique viewing angle or perspective of the user. The method also includes generating a 3D volume based on the 3D images and then generating a 3D mesh surface based on the 3D volume. The method further includes computing HRTFs associated with the user, based on the 3D mesh surface. Additionally, techniques are disclosed to provide a platform for sharing of the calculated HRTFs between audio applications executing on different devices.

Abstract: The present disclosure is directed to a system for generating immersive audio utilizing visual cues. In general, a system may be capable of transforming non-spatial audio (e.g., simple mono or stereo sound) associated with video into immersive sound (e.g., wherein sound may be generated spatially so that it appears to emanate directly from sound sources in the video). An example device may comprise data sourcing circuitry to receive multimedia data including at least video and non-spatial audio corresponding to the video, data analysis circuitry to determine at least one source of audio in the video and at least one attributable sound in the audio and audio generation circuitry to generate immersive audio wherein the at least one attributable sound is at least spatially associated with the at least one source of audio. Audio sources in the video may be determined using a variety of different types of detection.