Automated Proximity Discovery of Networked Cameras

Abstract

Automated discovery of the relative positioning of a network of cameras that view a physical environment. The automated discovery is based on comparing TimeLines for the cameras. The TimeLines are time-stamped data relating to the camera's view, for example a sequence of time stamps and corresponding images captured by a camera at those time stamps. In one approach, the relative positioning is represented by a proximity graph of nodes connected by edges. The nodes represent spaces in the physical environment, and each edge between two nodes represents a pathway between the spaces represented by the two nodes.

What is claimed is: 
     
         1 . A method implemented on a computer system for discovering a relative positioning of a network of cameras that view a physical environment, the method comprising:
 receiving TimeLines for the cameras, each TimeLine for a camera comprising a sequences of time stamps and data relating to the camera's view at those time stamps, the data including images captured by the camera at those time stamps;   comparing the TimeLines captured by the cameras; and   determining a proximity of spaces viewed by the cameras, based on the comparison of the TimeLines.   
     
     
         2 . The computer-implemented method of  claim 1  wherein the received TimeLines further comprise an identification of objects in the images, and the proximity of spaces is determined further based on the identification of the objects. 
     
     
         3 . The computer-implemented method of  claim 1  wherein the received TimeLines further comprise an estimated location of objects in the images, and the proximity of spaces is determined further based on the estimated location of the objects. 
     
     
         4 . The computer-implemented method of  claim 1  wherein the received TimeLines further comprise an estimated depth of objects in the images, and the proximity of spaces is determined further based on the estimated depth of the objects. 
     
     
         5 . The computer-implemented method of  claim 1  wherein the received TimeLines further comprise an estimated motion of objects in the images, and the proximity of spaces is determined further based on the estimated motion of the objects. 
     
     
         6 . The computer-implemented method of  claim 1  wherein the received TimeLines further comprise metadata describing of objects in the images, and the proximity of spaces is determined further based on the metadata describing the objects. 
     
     
         7 . The computer-implemented method of  claim 1  wherein the received TimeLines further comprise three-dimensional imagery, and the proximity of spaces is determined further based on the three-dimensional imagery. 
     
     
         8 . The computer-implemented method of  claim 1  wherein the received TimeLines further comprise multiples images associated with a single time stamp, and the proximity of spaces is determined further based on the multiple images. 
     
     
         9 . The computer-implemented method of  claim 1  wherein the received TimeLines further comprise visible and infrared images associated with a single time stamp, and the proximity of spaces is determined further based on the visible and infrared images. 
     
     
         10 . The computer-implemented method of  claim 1  wherein the received TimeLines further comprise SceneData for the spaces viewed by the cameras, and the proximity of spaces is determined further based on the SceneData. 
     
     
         11 . The computer-implemented method of  claim 1  wherein the network of cameras views rooms within a building, the method further comprising:
 analyzing an image captured by a camera to determine a type of room viewed by that camera, wherein the proximity of spaces is determined further based on the types of rooms viewed by cameras.