Abstract: A mechanism is described for facilitating protection and recovery of identities in surveillance camera environments according to one embodiment. An apparatus of embodiments, as described herein, includes detection and reception logic to receive a video stream of a scene as captured by a camera, wherein the scene includes persons. The apparatus may further include recognition and application logic to recognize an abnormal activity and one or more persons associated with the abnormal activity in a video frame of the video stream. The apparatus may further include identity recovery logic to recover one or more identities of the one or more persons in response to the abnormal activity, where the one or more identities are recovered from masked data and encrypted residuals associated with the one or more persons.

Abstract: In one example, a system for recognizing an object includes a processor to select from a plurality of image frames an image frame in which a view of the object is not blocked, and to estimate a location of the object in the selected image frame.

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: Methods and apparatus for controlled shadow casting to increase the perceptual quality of projected content are disclosed. In some examples, an apparatus is to increase a perceptual quality of content projected onto a projection surface. In some examples, the apparatus includes a processor and memory. In some examples, the memory includes computer readable instructions. In some examples, the instructions, when executed, cause the processor to determine a target shutter position for a shutter based on a location of a light source and a location of a projection surface. In some examples, the instructions, when executed, further cause the processor to move the shutter to the target shutter position to cast a shadow onto the projection surface around a portion of content projected onto the projection surface.

Abstract: An example apparatus for encoding video frames includes a receiver to receive video frames and a heat map from a camera and expected object regions from a video database. The apparatus also includes a region of interest (ROI) map generator to detect a region of interest in a video frame based on the expected object regions. The ROI map generator can also detect a region of interest in the video frame based on the heat map. The ROI map generator can then generate an ROI map based on the detected regions of interest. The apparatus further includes a parameter adjuster to adjust an encoding parameter based on the ROI map. The apparatus also further includes a video encoder to encode the video frame using the adjusted encoding parameter.

Abstract: An example apparatus for encoding video frames includes a receiver to receive events from a dynamic vision sensor and a video frame from an image sensor. The apparatus also includes a heat map generator to generate a heat map based on the received events. The apparatus further includes a region of interest (ROI) map generator generate a ROI map based on the heat map. The apparatus includes a parameter adjuster to adjust an encoding parameter based on the ROI map. The apparatus also further includes a video encoder to encode the video frame using the adjusted parameter.

Abstract: 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.

Abstract: Technologies for detecting interactions with surfaces from a spherical view of a room include a compute device. The compute device includes an image capture manager to obtain one or more images that depict a spherical view of a room that includes multiple surfaces. Additionally, the compute device includes a surface interaction detection manager to detect, from the one or more images, a person in the room, generate a bounding box around the person, preprocess the bounding box to represent the person in an upright orientation, determine a pose of the person from the preprocessed bounding box, detect an outstretched hand from the determined pose, and determine, from the detected outstretched hand, a surface of interaction in the room.

Abstract: Methods and apparatus for controlled shadow casting to increase the perceptual quality of projected content are disclosed. In some examples, an apparatus is to increase a perceptual quality of content projected onto a projection surface. In some examples, the apparatus includes a shutter position determiner to determine a target shutter position for a shutter based on a location of a light source and a location of the projection surface. In some disclosed examples, the apparatus further includes a shutter controller to move the shutter to the target shutter position to cast a shadow onto the projection surface around a portion of the content projected onto the projection surface.

Abstract: In one example, a system for recognizing an object includes a processor to select from a plurality of image frames an image frame in which a view of the object is not blocked, and to estimate a location of the object in the selected image frame.

Abstract: A method of interpolating a pixel value is disclosed and may include locating a missing pixel. Further, the method may include determining a plurality of closest pixels, determining a value for each of the plurality of closest pixels, and determining a distance between the missing pixel and each of the plurality of closest pixels. The method may also include classifying each of the plurality of closest pixels as either an edge-pixel or a non-edge pixel and determining a value of the missing pixel at least partially based on the value of each of the plurality of closest pixels, the distance between the missing pixel and each of the plurality of closest pixels, and a classification of each of the plurality of closest pixels.

Abstract: Motion estimation in video encoding switches among motion estimation methods for successive predicted pictures depending upon statistics of prior pictures. Locally averaged motion vectors, fraction of intra-coded macroblocks, average quantization parameter, and so forth provide tests for applicability of particular motion estimation methods, such as ones which perform better for large motion or perform better for small motion with complex texture.

Abstract: A method of interpolating a pixel value is disclosed and may include locating a missing pixel. Further, the method may include determining a plurality of closest pixels, determining a value for each of the plurality of closest pixels, and determining a distance between the missing pixel and each of the plurality of closest pixels. The method may also include classifying each of the plurality of closest pixels as either an edge-pixel or a non-edge pixel and determining a value of the missing pixel at least partially based on the value of each of the plurality of closest pixels, the distance between the missing pixel and each of the plurality of closest pixels, and a classification of each of the plurality of closest pixels.