Abstract: The present invention relates to a community security system capable of monitoring multiple, generally adjacent, consecutive private security areas to confirm human activity and engage sensory alerts. The community security system includes at least two private security areas having the capability of being networked together to enable information sharing. Each private security area is monitored by a private security system and contains one or more detection devices for monitoring and detecting activity occurring within the boundaries of the private security area. Information detected by detection devices within the private security areas can then communicate with other detection devices within the private security area or with the community security system to confirm activity, track activity across adjacent boundaries, engage sensory alerts and notify community members or authorities, as needed.

Abstract: Methods, systems and apparatuses may provide for technology that trains a neural network by inputting video data to the neural network, determining a boundary loss function for the neural network, and selecting weights for the neural network based at least in part on the boundary loss function, wherein the neural network outputs a pixel-level segmentation of one or more objects depicted in the video data. The technology may also operate the neural network by accepting video data and an initial feature set, conducting a tensor decomposition on the initial feature set to obtain a reduced feature set, and outputting a pixel-level segmentation of object(s) depicted in the video data based at least in part on the reduced feature set.

Abstract: Techniques related to automatically segmenting video frames into per pixel fidelity object of interest and background regions are discussed. Such techniques include applying tessellation to a video frame to generate feature frames corresponding to the video frame and applying a segmentation network implementing context aware skip connections to an input volume including the feature frames and a context feature volume corresponding to the video frame to generate a segmentation for the video frame.

Abstract: Techniques related to automatically segmenting video frames into per pixel fidelity object of interest and background regions are discussed. Such techniques include applying tessellation to a video frame to generate feature frames corresponding to the video frame and applying a segmentation network implementing context aware skip connections to an input volume including the feature frames and a context feature volume corresponding to the video frame to generate a segmentation for the video frame.

Abstract: Example apparatus disclosed include interface circuitry, machine readable instruction, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to access source input data and target input data, identify a domain shift prediction based on at least one of a feature decorrelation of the source input data or a feature decorrelation of the target input data, the domain shift prediction a source domain prediction or a target domain prediction, initiate gradient propagation of a domain loss to determine data features for the domain shift prediction, and rank input data features for the domain shift prediction.

Abstract: Methods, systems and apparatuses may provide for technology that trains a neural network by inputting video data to the neural network, determining a boundary loss function for the neural network, and selecting weights for the neural network based at least in part on the boundary loss function, wherein the neural network outputs a pixel-level segmentation of one or more objects depicted in the video data. The technology may also operate the neural network by accepting video data and an initial feature set, conducting a tensor decomposition on the initial feature set to obtain a reduced feature set, and outputting a pixel-level segmentation of object(s) depicted in the video data based at least in part on the reduced feature set.

Abstract: Techniques related to automatically segmenting a video frame into fine grain object of interest and background regions using a ground truth segmentation of an object in a previous frame are discussed. Such techniques apply multiple levels of segmentation tracking and prediction based on color, shape, and motion of the segmentation to determine per-pixel object probabilities, and solve an energy summation model to generate a final segmentation for the video frame using the object probabilities.

Abstract: The present invention relates to a community security system capable of monitoring multiple, generally adjacent, consecutive private security areas to confirm human activity and engage sensory alerts. The community security system includes at least two private security areas having the capability of being networked together to enable information sharing. Each private security area is monitored by a private security system and contains one or more detection devices for monitoring and detecting activity occurring within the boundaries of the private security area. Information detected by detection devices within the private security areas can then communicate with other detection devices within the private security area or with the community security system to confirm activity, track activity across adjacent boundaries, engage sensory alerts and notify community members or authorities, as needed.

Abstract: Methods, apparatus, and systems are disclosed for uncertainty estimation for human-in-the-loop automation (e.g., a human user or a machine user interview) using multi-view belief synthesis. An example apparatus includes at least one memory, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to receive input from a deep learning network, perform dissonance regularization to the input from the deep learning network, the dissonance regularization including a multi-view belief fusion, identify a loss function constraint based on the dissonance regularization, apply the identified loss function constraint during training of a viewpoint model, and initiate at least one user intervention based on a total vacuity threshold, the total vacuity threshold associated with the multi-view belief fusion.

Abstract: Techniques related to automatically segmenting video frames into per pixel dense object of interest and background regions are discussed. Such techniques include applying a segmentation convolutional neural network (CNN) to a CNN input including a current video frame, a previous video frame, an object of interest indicator frame, a motion frame, and multiple feature frames each including features compressed from feature layers of an object classification convolutional neural network as applied to the current video frame to generate candidate segmentations and selecting one of the candidate segmentations as a final segmentation of the current video frame.

Abstract: Techniques related to automatically segmenting a video frame into fine grain object of interest and background regions using a ground truth segmentation of an object in a previous frame are discussed. Such techniques apply multiple levels of segmentation tracking and prediction based on color, shape, and motion of the segmentation to determine per-pixel object probabilities, and solve an energy summation model to generate a final segmentation for the video frame using the object probabilities.

Abstract: An apparatus, method, system and computer readable medium for video tracking. An exemplar crop is selected to be tracked in an initial frame of a video. Bayesian optimization is applied with each subsequent frame of the video by building a surrogate model of an objective function using Gaussian Process Regression (GPR) based on similarity scores of candidate crops collected from a search space in a current frame of the video. A next candidate crop in the search space is determined using an acquisition function. The next candidate crop is compared to the exemplar crop using a Siamese neural network. Comparisons of new candidate crops to the exemplar crop are made using the Siamese neural network until the exemplar crop has been found in the current frame. The new candidate crops are selected based on an updated surrogate model.

Abstract: Data analysis and neural network training technology includes generates, based on a sparse neural network, a feature selection ranking representing a ranked list of features from input data, where the sparse neural network is a shallow neural network trained with the input data and then pruned, generates, based on the sparse neural network, a feature set dictionary representing interactions among features from the input data, and performs, based on the feature selection ranking and the feature set dictionary, one or more of generating an output analysis of insights from the input data and the sparse neural network, or training of a second neural network. The technology can also adjust the input data based on the feature set ranking to produce adjusted input data, where the sparse neural network is re-trained based on the adjusted input data and then pruned prior to generating the feature set dictionary.

Abstract: Techniques related to automatically segmenting video frames into per pixel dense object of interest and background regions are discussed. Such techniques include applying a segmentation convolutional neural network (CNN) to a CNN input including a current video frame, a previous video frame, an object of interest indicator frame, a motion frame, and multiple feature frames each including features compressed from feature layers of an object classification convolutional neural network as applied to the current video frame to generate candidate segmentations and selecting one of the candidate segmentations as a final segmentation of the current video frame.

Abstract: Disclosed is an example solution to analyze uncertainty of an evidential deep learning neural network with dissonance regularization and recurrent priors. An example apparatus includes processor circuitry to at least one of instantiate or execute the machine readable instructions to receive a first predicted classification of a first input of an evidential deep learning neural network (EVDL NN), identify a first uncertainty metric associated with the EVDL NN, the first uncertainty metric corresponding to the first input of the EVDL NN, calculate a first dissonance score based on the first uncertainty metric, and when the first dissonance score satisfies a threshold, assign the first predicted classification to the first input.

Abstract: Systems, apparatuses, and methods include technology that generates final frame predictions for a first plurality of frames of a video, where the first plurality of frames is associated with unlabeled data. The technology predicts an ordered list of actions for the first plurality of frames based on the final frame predictions, and temporally aligning the ordered list of actions to the final frame predictions to generate labels.