Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background subtraction algorithms define the background as parts of a scene that are at rest. Traditionally, these algorithms assume a stationary camera, and identify moving objects by detecting areas in a video that change over time. ,  extend the concept of ‘subtracting’ areas at rest to apply to video captured from a freely moving camera.  do not assume that the background is ll-approximated by a plane or that the camera center remains stationary during motion. The method operates entirely using 2D image measurements without requiring an explicit 3D reconstruction of the scene. A sparse model of background is built by robustly estimating a compact trajectory basis from trajectories of salient features across the video, and the background is ‘subtracted’ by removing trajectories that lie within the space spanned by the basis. Foreground and background appearance models are then built, and an optimal pixel-wise foreground/background labeling is obtained by efficiently maximizing a posterior function.
['Yaser Sheikh', 'Omar Javed', 'Takeo Kanade']
I am writing a research paper on a idea Background Subtraction for Freely Moving Cameras
[{"from": "human", "value": "I am writing a research paper on a idea Background Subtraction for Freely Moving Cameras"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Background subtraction algorithms define the background as parts of a scene that are at rest. Traditionally, these algorithms assume a stationary camera, and identify moving objects by detecting areas in a video that change over time. ,  extend the concept of \u2018subtracting\u2019 areas at rest to apply to video captured from a freely moving camera.  do not assume that the background is ll-approximated by a plane or that the camera center remains stationary during motion. The method operates entirely using 2D image measurements without requiring an explicit 3D reconstruction of the scene. A sparse model of background is built by robustly estimating a compact trajectory basis from trajectories of salient features across the video, and the background is \u2018subtracted\u2019 by removing trajectories that lie within the space spanned by the basis. Foreground and background appearance models are then built, and an optimal pixel-wise foreground/background labeling is obtained by efficiently maximizing a posterior function."}]