Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes a fast counting approach to estimate the number of people in indoor scenes. Firstly, a pre-processing step is used. In order to obtain a robust gray image in complex light conditions this step includes color correlation, image smoothing and contrast stretch. Secondly,  extract foreground region by background edge modeling and contour filling. Finally, after a foreground normalization based on camera calibration,  obtain the counting results with template matching. Experimental results show that compared with the Bayesian counting approach [2], our approach is robust to illumination variation and achieves a real-time counting result in indoor scenes.
['Ren Yang', 'Huazhong Xu', 'Jinqiao Wang']
I am writing a research paper on a idea Robust Crowd Segmentation and Counting in Indoor Scenes
[{"from": "human", "value": "I am writing a research paper on a idea Robust Crowd Segmentation and Counting in Indoor Scenes"}, {"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 fast counting approach to estimate the number of people in indoor scenes. Firstly, a pre-processing step is used. In order to obtain a robust gray image in complex light conditions this step includes color correlation, image smoothing and contrast stretch. Secondly,  extract foreground region by background edge modeling and contour filling. Finally, after a foreground normalization based on camera calibration,  obtain the counting results with template matching. Experimental results show that compared with the Bayesian counting approach [2], our approach is robust to illumination variation and achieves a real-time counting result in indoor scenes."}]