The present disclosure relates to a GIS geographical information retrieval method using a dynamic k-nearest neighbor search algorithm in an obstacle environment, which has been devised to search for geographical information by using a dynamic k-nearest neighbor search algorithm in an obstacle environment. The GIS geographical information retrieval method using a dynamic k-nearest neighbor search algorithm in an obstacle environment has an effect in that it can optimize a search time by considering obstacles as a range of angles, not discrete objects, on the basis of a query point, using the shortest path characteristics at the same time, and assigning priority to neighbors based on the calculated range of angles.

An object feature visualization system is disclosed. The system may include a computing device that generates video-mapped images to project onto physical objects. The video-mapped images may include features to be projected onto the objects. The projection of a video-mapped image onto the physical object allows for the visualization of the feature on the object. In some examples, the computing device receives a feature selection for a particular object, and generates a video-mapped image with the selected feature to provide to a projector to project the video-mapped image onto the physical object. In some examples, a user is able to select one or more features for one or more objects of a room display via a user interface. The system then projects video-mapped images with the selected features onto the physical objects. The system may allow a user to save feature selections, and to purchase or request additional information about objects with selected features.

An object feature visualization system is disclosed. The system may include a computing device that generates video-mapped images to project onto physical objects. The video-mapped images may include features to be projected onto the objects. The projection of a video-mapped image onto the physical object allows for the visualization of the feature on the object. In some examples, the computing device receives a feature selection for a particular object, and generates a video-mapped image with the selected feature to provide to a projector to project the video-mapped image onto the physical object. In some examples, a user is able to select one or more features for one or more objects of a room display via a user interface. The system then projects video-mapped images with the selected features onto the physical objects. The system may allow a user to save feature selections, and to purchase or request additional information about objects with selected features.

A video management system (VMS) may search for one or more events in a plurality of video streams captured and stored at a plurality of remote sites. The VMS may generate time-stamped metadata for each video stream captured at the remote site. The time-stamped metadata for each video stream may identify one or more objects and/or events occurring in the corresponding video stream as well as an identifier that uniquely identifies the corresponding video stream. Each of the plurality of remote sites may send the time-stamped metadata to a central hub, wherein the time-stamped metadata may be stored in a data lake, and a user may enter a query into a video query engine, wherein the video query engine may be operatively coupled to the central hub.

A video management system (VMS) may search for one or more events in a plurality of video streams captured and stored at a plurality of remote sites. The VMS may generate time-stamped metadata for each video stream captured at the remote site. The time-stamped metadata for each video stream may identify one or more objects and/or events occurring in the corresponding video stream as well as an identifier that uniquely identifies the corresponding video stream. Each of the plurality of remote sites may send the time-stamped metadata to a central hub, wherein the time-stamped metadata may be stored in a data lake, and a user may enter a query into a video query engine, wherein the video query engine may be operatively coupled to the central hub.

A geotagged video spatial indexing method for video retrieval based on a two-dimensional (2D) temporal grid is disclosed and includes: generating the 2D temporal grid by using the earliest start time, the latest end time of all geotagged video clips and a temporal resolution; calculating row and column number information of each geotagged video clip based on its start time and end time; generating a spatial point for each geotagged video clip based on the row and column number information and obtaining a spatial point set; generating a R-tree spatial index structure corresponding to the spatial point set using a R-tree spatial index method; and locating the corresponding cell in a temporal grid based on retrieval conditions, generating a spatial point based on row and column number information of the grid cell, and finding the spatial point in the R-tree spatial index structure to get the geotagged video corresponding thereto.

A geotagged video spatial indexing method for video retrieval based on a two-dimensional (2D) temporal grid is disclosed and includes: generating the 2D temporal grid by using the earliest start time, the latest end time of all geotagged video clips and a temporal resolution; calculating row and column number information of each geotagged video clip based on its start time and end time; generating a spatial point for each geotagged video clip based on the row and column number information and obtaining a spatial point set; generating a R-tree spatial index structure corresponding to the spatial point set using a R-tree spatial index method; and locating the corresponding cell in a temporal grid based on retrieval conditions, generating a spatial point based on row and column number information of the grid cell, and finding the spatial point in the R-tree spatial index structure to get the geotagged video corresponding thereto.

An example operation may include one or more of receiving, by an accident processing node, an accident report generated by a transport, retrieving, by the accident processing node, a time and a location of the accident from the accident report, querying, by the accident processing node, a plurality of transport profiles on a storage based on the time and the location of the accident, and retrieving video data associated with the time and the location of the accident from the plurality of the transport profiles.

Techniques for analyzing media content are described. One technique generally comprises performing a regression analysis for characters in a plurality of media content based on user demographics, content outcome measure, and character models. The technique determines an attribute of significance. In some embodiments, the technique selects media content for display that depicts a character having at least a threshold value of the attribute of significance. In some embodiments, the technique displays media analytics for the attribute of significance determined based on a value of the attribute of significance exceeding a threshold significance value.

In accordance with an embodiment, described herein is a system and method for predicting artists that create media content who are more likely to increase in popularity. Users are determined who requested playback of media content items associated with one or more generators of popular media content within a window of time. One or more early adopters are determined from these users based on a quantity of the one or more generators of popular media content whose media content items were requested for playback by the users. Artists that create media content who are more likely to increase in popularity than other artists that create media content are then predicted based on following further requested playback of media content items by the one or more early adopters.