Systems and methods for location identification and tracking of a person, object and/or vehicle. The methods involve: obtaining, by a computing system, a video of a surrounding environment which was captured by a portable camera coupled to the person, object or vehicle; comparing, by the computing system, first images of the video to pre-stored second images to identify geographic locations where the first images were captured by the portable camera; analyzing, by the computing system, the identified geographic locations to verify that the person, object or vehicle is (1) traveling along a correct path, (2) traveling towards a facility for which the person, object or vehicle has authorization to enter, or (3) traveling towards a zone or secured area internal or external to the facility for which the person, object or vehicle has authorization to enter; and transmitting a notification from the computing system indicating the results of the analyzing.

Systems, methods, devices and non-transitory, computer-readable storage mediums are disclosed for a wearable multimedia device and cloud computing platform with an application ecosystem for processing multimedia data captured by the wearable multimedia device. In an embodiment, a method comprises: receiving, by one or more processors of a cloud computing platform, context data from a wearable multimedia device, the wearable multimedia device including at least one data capture device for capturing the context data; creating a data processing pipeline with one or more applications based on one or more characteristics of the context data and a user request; processing the context data through the data processing pipeline; and sending output of the data processing pipeline to the wearable multimedia device or other device for presentation of the output.

An acoustic environment identification system is disclosed that can use neural networks to accurately identify environments. The acoustic environment identification system can use one or more convolutional neural networks to generate audio feature data. A recursive neural network can process the audio feature data to generate characterization data. The characterization data can be modified using a weighting system that weights signature data items. Classification neural networks can be used to generate a classification of an environment.

An acoustic environment identification system is disclosed that can use neural networks to accurately identify environments. The acoustic environment identification system can use one or more convolutional neural networks to generate audio feature data. A recursive neural network can process the audio feature data to generate characterization data. The characterization data can be modified using a weighting system that weights signature data items. Classification neural networks can be used to generate a classification of an environment.

A system and method for risk object identification via causal inference that includes receiving at least one image of a driving scene of an ego vehicle and analyzing the at least one image to detect and track dynamic objects within the driving scene of the ego vehicle. The system and method also include implementing a mask to remove each of the dynamic objects captured within the at least one image. The system and method further include analyzing a level of change associated with a driving behavior with respect to a removal of each of the dynamic objects. At least one dynamic object is identified as a risk object that has a highest level of influence with respect to the driving behavior.

A target tracking method and apparatus is provided. The target tracking apparatus includes a memory configured to store a neural network, and a processor configured to extract feature information of each of a target included in a target region in a first input image, a background included in the target region, and a searching region in a second input image, using the neural network, obtain similarity information of the target and the searching region and similarity information of the background and the searching region based on the extracted feature information, obtain a score matrix including activated feature values based on the obtained similarity information, and estimate a position of the target in the searching region from the score matrix.

A computer-implemented method for preventing unauthorized processing of a digital representation of at least a portion of a document, a device for data processing, and a computer program product are provided, wherein in particular the document is a banknote. The method comprises providing data, wherein the data is based on the digital representation of at least a portion of a test element. The digital representation may be an image file corresponding to the at least one portion of the test element. The method also involves analyzing the data with regard to data representing at least one characterizing feature of the at least one portion of the document. The method further comprises activating prohibiting means if the data being based on the digital representation of the at least one portion of the test element is similar to the data representing the at least one characterizing feature. The prohibiting means prohibit the data being based on the digital representation of the at least one portion of the test element to be further processed, in particular comprising copying and/or transmitting and/or printing and/or reproducing the data. Alternatively, the prohibiting means amend the data such that the data is prevented from being transmitted and/or printed and/or reproduced and/or further amended by data processing devices.

A method for identifying authenticity of an object, the method includes maintaining, in an identification server system, a reference image of an original object, the reference image and provided to represent all equivalent original objects, receiving, in the identification server system, one or more input images of the object to be identified, and generating, by the identification server system, a target image from the one or more input images. The method further includes aligning, by the identification server system, the target image with the reference image and analysing, by the identification server system, the target image in relation to the aligned reference image for identifying authenticity of the object.

Provided are a scene detection method and apparatus, an electronic device and a computer storage medium. The method includes: own device running data and scene information collected by a collection device are acquired through an own first detection system component; the device running data and the scene information are made pulled to a cloud for the cloud to detect itself and the collection device; in a case of a detection exception of the cloud, an own first device state is detected according to the device running data, and a second device state of the collection device is detected based on the scene information; and in a case where both the first device state and the second device state are normal, a scene state is detected according to a present frame of scene image in the scene information and a locally stored configuration file to obtain a detection result for use.

A method for human-object interaction detection includes receiving an image. A set of features are extracted from multiple positions of the image. One or more human-object pairs may be predicted based on the extracted set of features. A human-object interaction may be determined based on a set of candidate interactions and the predicted human-object pairs.