Abstract: Causal explanations of outputs of a neural network can be learned from an attention layer in the neural network. The neural network may compute an output variable by processing a variable set including one or more input variables. An attention matrix may be computed by the attention layer in an abductive inference for which a new variable set including the input variables and the output variable is input into the neural network. Causal relationship between the variables in the new variable set may be determined based on the attention matrix and illustrated in a causal graph. A tree structure may be generated based on the causal graph. An input variable may be identified using the tree structure and determined to be the reason why the neural network computed the output variable. An explanation of the causal relation between the input variable and output variable can be generated and provided.

Abstract: A system and method for dynamically ordering video streams on display in a command and control system according to video content, including obtaining a plurality of video streams captured by a plurality of respective video cameras; extracting features from the plurality of video streams; calculating abnormality scores for the extracted features, wherein each abnormality score measures a likelihood that a corresponding feature is an abnormal feature; calculating a current rank for ordering each of the plurality of video streams based on current and past abnormality scores of the features extracted from the video stream; and displaying a subgroup of the plurality of video streams having the highest subset of current ranks based on abnormality scores.

Abstract: Various embodiments are generally directed to techniques for determining artificial neural network topologies, such as by utilizing probabilistic graphical models, for instance. Some embodiments are particularly related to determining neural network topologies by bootstrapping a graph, such as a probabilistic graphical model, into a multi-graphical model, or graphical model tree. Various embodiments may include logic to determine a collection of sample sets from a dataset. In various such embodiments, each sample set may be drawn randomly for the dataset with replacement between drawings. In some embodiments, logic may partition a graph into multiple subgraph sets based on each of the sample sets. In several embodiments, the multiple subgraph sets may be scored, such as with Bayesian statistics, and selected amongst as part of determining a topology for a neural network.

Abstract: A system and method for dynamically ordering video streams on display in a command and control system according to video content, including obtaining a plurality of video streams captured by a plurality of respective video cameras; extracting features from the plurality of video streams; calculating abnormality scores for the extracted features, wherein each abnormality score measures a likelihood that a corresponding feature is an abnormal feature; calculating a current rank for ordering each of the plurality of video streams based on current and past abnormality scores of the features extracted from the video stream; and displaying a subgroup of the plurality of video streams having the highest subset of current ranks based on abnormality scores.

Abstract: A system and method for dynamically ordering video streams on display in a command and control system according to video content, including obtaining a plurality of video streams captured by a plurality of respective video cameras; extracting features from the plurality of video streams; calculating abnormality scores for the extracted features, wherein each abnormality score measures a likelihood that a corresponding feature is an abnormal feature; calculating a current rank for ordering each of the plurality of video streams based on current and past abnormality scores of the features extracted from the video stream; and displaying a subgroup of the plurality of video streams having the highest subset of current ranks based on abnormality scores.

Abstract: A system and method for dynamically ordering video streams on display in a command and control system according to video content, including obtaining a plurality of video streams captured by a plurality of respective video cameras; extracting features from the plurality of video streams; calculating abnormality scores for the extracted features, wherein each abnormality score measures a likelihood that a corresponding feature is an abnormal feature; calculating a current rank for ordering each of the plurality of video streams based on current and past abnormality scores of the features extracted from the video stream; and displaying a subgroup of the plurality of video streams having the highest subset of current ranks based on abnormality scores.

Abstract: Various embodiments are generally directed to techniques for determining artificial neural network topologies, such as by utilizing probabilistic graphical models, for instance. Some embodiments are particularly related to determining neural network topologies by bootstrapping a graph, such as a probabilistic graphical model, into a multi-graphical model, or graphical model tree. Various embodiments may include logic to determine a collection of sample sets from a dataset. In various such embodiments, each sample set may be drawn randomly for the dataset with replacement between drawings. In some embodiments, logic may partition a graph into multiple subgraph sets based on each of the sample sets. In several embodiments, the multiple subgraph sets may be scored, such as with Bayesian statistics, and selected amongst as part of determining a topology for a neural network.

Abstract: A system and method for detecting an object of interest. A system and method may generate a first signature for an object of interest based on an image of the object of interest. A system and method may generate a second signature for a candidate object based on an image of the candidate object. A system and method may calculate a similarity score by relating the first signature to the second signature and may determine the image of the candidate object is an image of the object of interest based on the similarity score.

Abstract: A system and method for detecting a particular human in a plurality of images of humans may include one or more processors to receive input data describing the appearance of the particular human via a graphical user interface (GUI). An image representing the particular human may be generated based on the input data. This may take the form of an avatar or artificial image. This artificial or processor-generated image may be used to identify one or more of the humans in said plurality of images as a candidate for the particular human.

Abstract: A system and method for detecting an object of interest. A system and method may generate a first signature for an object of interest based on an image of the object of interest. A system and method may generate a second signature for a candidate object based on an image of the candidate object. A system and method may calculate a similarity score by relating the first signature to the second signature and may determine the image of the candidate object is an image of the object of interest based on the similarity score.

Abstract: A system or method for automatic calculation of scene geometry may include receiving, by a processor, a video stream including a plurality of video frames. A video frame may be divided into blocks, and a vertical vanishing point of the video stream may be determined based on a dominant orientation of detected edges in the blocks. A horizon and scale of the video stream may be estimated based on optical flow of pixels between two consecutive video frames. A set of human heads may be detected in a video frame of the video stream based on the estimated horizon and scale of the video stream.

Abstract: A system and method for detecting an object of interest. A system and method may generate a first signature for an object of interest based on an image of the object of interest. A system and method may generate a second signature for a candidate object based on an image of the candidate object. A system and method may calculate a similarity score by relating the first signature to the second signature and may determine the image of the candidate object is an image of the object of interest based on the similarity score.

Abstract: A system or method for automatic calculation of scene geometry may include receiving, by a processor, a video stream including a plurality of video frames. A video frame may be divided into blocks, and a vertical vanishing point of the video stream may be determined based on a dominant orientation of detected edges in the blocks. A horizon and scale of the video stream may be estimated based on optical flow of pixels between two consecutive video frames. A set of human heads may be detected in a video frame of the video stream based on the estimated horizon and scale of the video stream.

Abstract: A system and method for detecting a particular human in a plurality of images of humans may include one or more processors to receive input data describing the appearance of the particular human via a graphical user interface (GUI). An image representing the particular human may be generated based on the input data. This may take the form of an avatar or artificial image. This artificial or processor-generated image may be used to identify one or more of the humans in said plurality of images as a candidate for the particular human.

Abstract: A system and method for detecting an object of interest. A system and method may generate a first signature for an object of interest based on an image of the object of interest. A system and method may generate a second signature for a candidate object based on an image of the candidate object. A system and method may calculate a similarity score by relating the first signature to the second signature and may determine the image of the candidate object is an image of the object of interest based on the similarity score.

Abstract: Embodiments of the invention may include generating a description of a monitored scene and creating a plurality of rules related to the monitored scene by using common language. The description of the monitored scene and the plurality of rules may be converted into system parameters and an analysis of the video data may be performed based on the system parameters. The results of the analysis may be converted into one or more output terms from the plurality of predefined terms and may be displayed to a user in a common language.

Abstract: Embodiments of the invention may include generating a description of a monitored scene and creating a plurality of rules related to the monitored scene by using common language. The description of the monitored scene and the plurality of rules may be converted into system parameters and an analysis of the video data may be performed based on the system parameters. The results of the analysis may be converted into one or more output terms from the plurality of predefined terms and may be displayed to a user in a common language.