Abstract: Systems and methods of determining image characteristics are provided. More particularly, a first image having an unknown characteristic can be obtained. The first image can be provided to a plurality of user devices in a verification challenge. The verification challenge can include one or more instructions to be presented to a user of each user device. The instructions being determined based at least in part on the first image. User responses can be received, and an unknown characteristic of the first image can be determined based at least in part on the received responses. Subsequent to determining the unknown characteristic of the first image, one or more machine learning models can be trained based at least in part on the determined characteristic.

Abstract: Computer-implemented methods and systems for automatically classifying businesses from imagery can include providing one or more images of a location entity as input to a statistical model that can be applied to each image. A plurality of classification labels for the location entity in the one or more images can be generated and provided as an output of the statistical model. The plurality of classification labels can be generated by selecting from an ontology that identifies predetermined relationships between location entities and categories associated with corresponding classification labels at multiple levels of granularity. Confidence scores for the plurality of classification labels can be generated to indicate a likelihood level that each generated classification label is accurate for its corresponding location entity. Associations based on the classification labels generated for each image can be stored in a database and used to help retrieve relevant business information requested by a user.

Abstract: Aspects of the present disclosure relate to a method includes training a deep neural network using training images and data identifying one or more business storefront locations in the training images. The deep neural network outputs tight bounding boxes on each image. At the deep neural network, a first image may be received. The first image may be evaluated using the deep neural network. Bounding boxes may then be generated identifying business storefront locations in the first image.

Abstract: Aspects of the present disclosure relate to a method includes training a deep neural network using training images and data identifying one or more business storefront locations in the training images. The deep neural network outputs tight bounding boxes on each image. At the deep neural network, a first image may be received. The first image may be evaluated using the deep neural network. Bounding boxes may then be generated identifying business storefront locations in the first image.

Abstract: Automated person re-identification may be assisted by consideration of attributes of the person in a joint classification with matching of the person. By both solving for similarities in a plurality of attributes and identities, discriminative interactions may be captured. Automated person re-identification may be assisted by consideration of a semantic color name. Rather than a color histogram, probability distributions are mapped to color terms of the semantic color name. Using other descriptors as well, similarity measures for the various descriptors are weighted and combined into a score. Either or both considerations may be used.

Abstract: Multiple cameras are configured for use in video analytics. A single configuration tool is provided. The interrelationships between cameras are included within the configuration. Using a combination of text entry fields, registration of the cameras on a floor or other map, and marking on images from the cameras, an efficient workflow for configuration may be provided.

Abstract: A forensic video search user interface is disclosed that accesses databases of stored video event metadata from multiple camera streams and facilitates the workflow of search of complex global events that are composed of a number of simpler, low complexity events.

Abstract: A method for retrieving information spread across a plurality of different ontologies, including: defining a meta-ontology, wherein the meta-ontology includes high-level properties and their mappings to specific properties defined in a plurality of different ontologies; receiving a question, wherein the question is associated with a high-level property; and providing an answer to the question, wherein the answer is determined by using the meta-ontology.

Abstract: A method for tracking pedestrians in a video sequence, where each image frame of the video sequence corresponds to a time step, includes using marginal space learning to sample a prior probability distribution p(xt|Zt?1) of multi-person identity assignments given a set of feature measurements from all previous image frames, using marginal space learning to estimate an observation likelihood distribution p(zt|xt) of the set of features given a set of multi-person identity assignments sampled from the prior probability distribution, calculating a posterior probability distribution p(xt|Zt) from the observation likelihood distribution p(zt|xt) and the prior probability distribution p(xt|Zt?1), and using marginal space learning to estimate the prior probability distribution p(xt+1|Zt) for a next image frame given the posterior probability distribution p(xt|Zt) and a probability p(xt+1|xt), where the posterior probability distribution of multi-person identity assignments corresponds to a set of pedestrian detectio

Abstract: A method of detecting an object in image data that is deemed to be a threat includes annotating sections of at least one training image to indicate whether each section is a component of the object, encoding a pattern grammar describing the object using a plurality of first order logic based predicate rules, training distinct component detectors to each identify a corresponding one of the components based on the annotated training images, processing image data with the component detectors to identify at least one of the components, and executing the rules to detect the object based on the identified components.