Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a moving object detection algorithm adapting to various scene changes in a moving camera. In the moving camera scene, both backgrounds and objects are moving while the level of illumination in general varies frequently. To handle these scene changes,  propose a scene conditional background update scheme that adaptively builds the background according to how the scene changes. First,  estimate the three scene condition variables of background motion, foreground motion and illumination changes for an awareness of the scene condition.  then compensate for the camera movement and update the background model in different ways according to the scene condition. Lastly,  propose a new foreground decision method with a foreground likelihood map, two thresholds, and a watershed algorithm to generate a spatially connected foreground region.  validate the effectiveness of our method quantitatively and qualitatively with ten videos in various scene conditions. The experimental results show that our method adapts itself to dynamic scene changes and outperforms state-of-the-art methods.
['Kimin Yun', 'Jongin Lim', 'Jin Young Choi']
I am writing a research paper on a idea Scene conditional background update for moving object detection in a moving camera
[{"from": "human", "value": "I am writing a research paper on a idea Scene conditional background update for moving object detection in a moving camera"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a moving object detection algorithm adapting to various scene changes in a moving camera. In the moving camera scene, both backgrounds and objects are moving while the level of illumination in general varies frequently. To handle these scene changes,  propose a scene conditional background update scheme that adaptively builds the background according to how the scene changes. First,  estimate the three scene condition variables of background motion, foreground motion and illumination changes for an awareness of the scene condition.  then compensate for the camera movement and update the background model in different ways according to the scene condition. Lastly,  propose a new foreground decision method with a foreground likelihood map, two thresholds, and a watershed algorithm to generate a spatially connected foreground region.  validate the effectiveness of our method quantitatively and qualitatively with ten videos in various scene conditions. The experimental results show that our method adapts itself to dynamic scene changes and outperforms state-of-the-art methods."}]