Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: It has been shown that under a small number of assumptions, observations of people can be used to obtain metric calibration information of a camera, which is particularly useful for surveillance applications. Hover, previous work had to exclude the common criticial configuration of the camera’s principal point falling on the horizon line and very long focal lengths, both of which occur commonly in practise. Due to noise, the quality of the calibration quickly degrades at and in the vicinity of these configurations.  provides a robust solution to this problem by incorporating information about the motion of people into the estimation process. It is shown that under the assumption that people walk with a constant velocity, calibration performance can be improved significantly. In addition to solving the above problem, the incorporation of temporal data also helps to take correlations beten subsequent detections into consideration, which leads to an up-front reduction of the noise in the measurements and an overall improvement in auto-calibration performance.
['Nils Krahnstoever', 'Paulo R. S. Mendonça']
I am writing a research paper on a idea Autocalibration from Tracks of Walking People
[{"from": "human", "value": "I am writing a research paper on a idea Autocalibration from Tracks of Walking People"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: It has been shown that under a small number of assumptions, observations of people can be used to obtain metric calibration information of a camera, which is particularly useful for surveillance applications. Hover, previous work had to exclude the common criticial configuration of the camera\u2019s principal point falling on the horizon line and very long focal lengths, both of which occur commonly in practise. Due to noise, the quality of the calibration quickly degrades at and in the vicinity of these configurations.  provides a robust solution to this problem by incorporating information about the motion of people into the estimation process. It is shown that under the assumption that people walk with a constant velocity, calibration performance can be improved significantly. In addition to solving the above problem, the incorporation of temporal data also helps to take correlations beten subsequent detections into consideration, which leads to an up-front reduction of the noise in the measurements and an overall improvement in auto-calibration performance."}]