Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An automatic object tracking and video summarization method for multi-camera systems with a large number of non-overlapping field-of-view cameras is explained. In this framework, video sequences are stored for each object as opposed to storing a sequence for each camera. Object-based representation enables annotation of video segments, and extraction of content semantics for further analysis.  also present a novel solution to the inter-camera color calibration problem. The transitive model function enables effective compensation for lighting changes and radiometric distortions for large-scale systems. After initial calibration, objects are tracked at each camera by background subtraction and mean-shift analysis. The correspondence of objects beten different cameras is established by using a Bayesian belief network. This framework empors the user to get a concise response to queries such as "which locations did an object visit on Monday and what did it do there?".
['Fatih Porikli', 'Ajay Divakaran']
I am writing a research paper on a idea Multi-camera calibration, object tracking and query generation
[{"from": "human", "value": "I am writing a research paper on a idea Multi-camera calibration, object tracking and query generation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: An automatic object tracking and video summarization method for multi-camera systems with a large number of non-overlapping field-of-view cameras is explained. In this framework, video sequences are stored for each object as opposed to storing a sequence for each camera. Object-based representation enables annotation of video segments, and extraction of content semantics for further analysis.  also present a novel solution to the inter-camera color calibration problem. The transitive model function enables effective compensation for lighting changes and radiometric distortions for large-scale systems. After initial calibration, objects are tracked at each camera by background subtraction and mean-shift analysis. The correspondence of objects beten different cameras is established by using a Bayesian belief network. This framework empors the user to get a concise response to queries such as \"which locations did an object visit on Monday and what did it do there?\"."}]