Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Non-parametric representation of pixel intensity distribution is quite effective to construct proper background model and to detect foreground objects accurately. Hover, from the viewpoint of practical application, the computation cost of the distribution estimation should be reduced. ,  present fast estimation of the probability density function (PDF) of pixel value using Parzen density estimation and foreground object detection based on the estimated PDF. Here, the PDF is computed by partially updating the PDF estimated at the previous frame, and it greatly reduces the computation cost of the PDF estimation. Thus, the background model adapts quickly to changes in the scene and, therefore, foreground objects can be robustly detected. Several experiments show the effectiveness of our approach.
['Tatsuya Tanaka', 'Atsushi Shimada', 'Daisaku Arita', 'Rin-ichiro Taniguchi']
I am writing a research paper on a idea A fast algorithm for adaptive background model construction using parzen density estimation
[{"from": "human", "value": "I am writing a research paper on a idea A fast algorithm for adaptive background model construction using parzen density estimation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Non-parametric representation of pixel intensity distribution is quite effective to construct proper background model and to detect foreground objects accurately. Hover, from the viewpoint of practical application, the computation cost of the distribution estimation should be reduced. ,  present fast estimation of the probability density function (PDF) of pixel value using Parzen density estimation and foreground object detection based on the estimated PDF. Here, the PDF is computed by partially updating the PDF estimated at the previous frame, and it greatly reduces the computation cost of the PDF estimation. Thus, the background model adapts quickly to changes in the scene and, therefore, foreground objects can be robustly detected. Several experiments show the effectiveness of our approach."}]