Video: Innovation – Characterizing Properties of Gray Assets

This video illustrates a research project focused on characterizing properties of gray assets. Cameras are ubiquitous public assets in many large cities, and can be important assets for information gathering during urban operations. Such public cameras often have limited metadata describing their attributes. A key missing attribute is the precise location of the camera, using which it is possible to precisely pinpoint the location of events seen in the camera. In this work, we explore the following question: under what conditions is it possible to estimate the location of a camera from a single image taken by the camera? We show that, using a judicious combination of projective geometry, neural networks, and crowd-sourced annotations from human workers, it is possible to position 95% of the images in our test data set to within 12m. This performance is two orders of magnitude better than PoseNet, a state-of-the-art neural network that, when trained on a large corpus of images in an area, can estimate the pose of a single image. Finally, we show that the camera’s inferred position and intrinsic parameters can help design a number of virtual sensors, all of which have less than 10-15% error with high percentile. (THERE IS NO AUDIO IN THIS VIDEO)