Abstract: Methods, systems, and techniques for performing a facet search include receiving facet search commencement user input indicating that a search for a facet is to commence; in response to the facet search commencement user input, searching one or more video recordings for the facet; and displaying, on a display, facet image search results depicting the facet, wherein the facet image search results are selected from the one or more video recordings. An artificial neural network may be used for the facet search, and that network may be trained by generating a facet image training set that comprises training images, with the training images depicting a type of facet common to the training images; and training, by using the facet image training set, that neural network to classify the type of facet when a sample image comprising the type of facet is input to that network.

Abstract: Generating signatures within a network that includes a plurality of computing devices of varying processing capabilities is disclosed. Chips may be transmitted, from a network video recorder and over the network, to an analytics appliance having a GPU processing power that is higher than a GPU processing power possessed by the network video recorder. The GPU processing power possessed by the analytics appliance may be employed to process the chips therein and generate respective signatures.

Abstract: A method is provided, comprising: receiving at a camera system comprising a plurality of video cameras, a search request from a mobile device, the search request comprising a geographic location of the mobile device and a time range; identifying one or more video cameras of the plurality of video cameras having a field of view including the geographic location; and transmitting search results from the one or more video cameras of the plurality of video cameras to the mobile device, the search results comprising at least an image from a video recorded by the one or more video cameras of the plurality of video cameras during the time range.

Abstract: A method for performing an appearance search using a map involves receiving search commencement input requesting that an appearance search for one or more objects-of-interest commence be performed. In response to the search commencement input, one or more video recordings for the one or more objects-of-interest are searched. In conjunction with a map on a display, one or more appearance search results depicting the one or more objects-of-interest are then displayed. Each of the appearance search results depicts the one or more objects-of-interest as captured by a camera at a time during the one or more video recordings, and is depicted in conjunction with the map at a location indicative of a geographical location of the camera. The method may be performed using a video surveillance system and stored on one or more computer-readable media for execution using that system.

Abstract: There are described methods and systems for facilitating identification of an object-of-interest. A face similarity score and a body similarity score of a query image relative to a gallery image are determined. A fused similarity score of the query image relative to the gallery image is determined by applying a relationship between the face similarity score, the body similarity score, and the fused similarity score. The fused similarity score is indicative of whether or not the object-of-interest and the potential object-of-interest are the same object-of-interest. For example, a machine learning process is used to fuse the face similarity score and the body similarity into the fused similarity score. The process is repeated for multiple gallery images. The gallery images may then be ranked according to their respective fused similarity scores.

Abstract: Methods, systems, and techniques for performing a facet search include receiving facet search commencement user input indicating that a search for a facet is to commence; in response to the facet search commencement user input, searching one or more video recordings for the facet; and displaying, on a display, facet image search results depicting the facet, wherein the facet image search results are selected from the one or more video recordings. An artificial neural network may be used for the facet search, and that network may be trained by generating a facet image training set that comprises training images, with the training images depicting a type of facet common to the training images; and training, by using the facet image training set, that neural network to classify the type of facet when a sample image comprising the type of facet is input to that network.

Abstract: Generating signatures within a network that includes a plurality of computing devices of varying processing capabilities is disclosed. Chips may be transmitted, from a network video recorder and over the network, to an analytics appliance having a GPU processing power that is higher than a GPU processing power possessed by the network video recorder. The GPU processing power possessed by the analytics appliance may be employed to process the chips therein and generate respective signatures.

Abstract: There are described methods and systems for facilitating identification of an object-of-interest. A face similarity score and a body similarity score of a query image relative to a gallery image are determined. A fused similarity score of the query image relative to the gallery image is determined by applying a relationship between the face similarity score, the body similarity score, and the fused similarity score. The fused similarity score is indicative of whether or not the object-of-interest and the potential object-of-interest are the same object-of-interest. For example, a machine learning process is used to fuse the face similarity score and the body similarity into the fused similarity score. The process is repeated for multiple gallery images. The gallery images may then be ranked according to their respective fused similarity scores.