Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: When tracking multiple targets with autonomous cameras for 3D scene reconstruction, e.g., in sports, a significant challenge is handling the unpredictable nature of the targets' motion. Such a monitoring system must reposition according to the targets' movements and maintain satisfactory coverage of the targets.  propose an approximate, centralized approach for maximizing the visible boundary of dynamic targets using mobile cameras in a bounded 2D environment. Targets and obstacles translate, rotate, and deform independently, and cameras are only aware of the current position and shape of the targets and obstacles. Using current information, the environment is searched for better viewing positions, then cameras navigate to those positions while avoiding collisions with targets and obstacles.  present a benchmark and metrics to evaluate the performance of our method, and compare our approach to a simple gradient-based local method in several real-time simulations.
['Wolfgang Hönig', 'Nora Ayanian']
I am writing a research paper on a idea Dynamic multi-target coverage with robotic cameras
[{"from": "human", "value": "I am writing a research paper on a idea Dynamic multi-target coverage with robotic cameras"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: When tracking multiple targets with autonomous cameras for 3D scene reconstruction, e.g., in sports, a significant challenge is handling the unpredictable nature of the targets' motion. Such a monitoring system must reposition according to the targets' movements and maintain satisfactory coverage of the targets.  propose an approximate, centralized approach for maximizing the visible boundary of dynamic targets using mobile cameras in a bounded 2D environment. Targets and obstacles translate, rotate, and deform independently, and cameras are only aware of the current position and shape of the targets and obstacles. Using current information, the environment is searched for better viewing positions, then cameras navigate to those positions while avoiding collisions with targets and obstacles.  present a benchmark and metrics to evaluate the performance of our method, and compare our approach to a simple gradient-based local method in several real-time simulations."}]