Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a novel approach to motion detection in scenes captured from a camera onboard an aerial vehicle. In particular,  are interested in detecting small objects such as cars or people that move slowly and independently in the scene. Slow motion detection in an aerial video is challenging because it is difficult to differentiate object motion from camera motion.  adopt an unsupervised learning approach that requires a grouping step to define slow object motion. The grouping is done by building a graph of edges connecting dense feature keypoints. Then,  use camera motion constraints over a window of adjacent frames to compute a ight for each edge and automatically prune away dissimilar edges. This leaves us with groupings of similarly moving feature points in the space, which  cluster and differentiate as moving objects and background. With a focus on surveillance from a moving aerial platform,  test our algorithm on the challenging VIRAT aerial data set [1] and provide qualitative and quantitative results that demonstrate the effectiveness of our detection approach.
['Joseph DeGol', 'Myra Nam']
I am writing a research paper on a idea A clustering approach for detecting moving objects captured by a moving aerial camera
[{"from": "human", "value": "I am writing a research paper on a idea A clustering approach for detecting moving objects captured by a moving aerial camera"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a novel approach to motion detection in scenes captured from a camera onboard an aerial vehicle. In particular,  are interested in detecting small objects such as cars or people that move slowly and independently in the scene. Slow motion detection in an aerial video is challenging because it is difficult to differentiate object motion from camera motion.  adopt an unsupervised learning approach that requires a grouping step to define slow object motion. The grouping is done by building a graph of edges connecting dense feature keypoints. Then,  use camera motion constraints over a window of adjacent frames to compute a ight for each edge and automatically prune away dissimilar edges. This leaves us with groupings of similarly moving feature points in the space, which  cluster and differentiate as moving objects and background. With a focus on surveillance from a moving aerial platform,  test our algorithm on the challenging VIRAT aerial data set [1] and provide qualitative and quantitative results that demonstrate the effectiveness of our detection approach."}]