Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes the problem of modeling video sequences of dynamic swarms (DSs).  define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS,  present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally.  assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework,  iterate beten learning the spatial layout of the swarm and its dynamics.  learn the swarm transformations using ICM, which iterates beten estimating these transformations and updating their distribution in the spatiotemporal neighborhoods.  demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data.
['Bernard Ghanem', 'Narendra Ahuja']
I am writing a research paper on a idea Modeling dynamic swarms
[{"from": "human", "value": "I am writing a research paper on a idea Modeling dynamic swarms"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  proposes the problem of modeling video sequences of dynamic swarms (DSs).  define a DS as a large layout of stochastically repetitive spatial configurations of dynamic objects (swarm elements) whose motions exhibit local spatiotemporal interdependency and stationarity, i.e., the motions are similar in any small spatiotemporal neighborhood. Examples of DS abound in nature, e.g., herds of animals and flocks of birds. To capture the local spatiotemporal properties of the DS,  present a probabilistic model that learns both the spatial layout of swarm elements (based on low-level image segmentation) and their joint dynamics that are modeled as linear transformations. To this end, a spatiotemporal neighborhood is associated with each swarm element, in which local stationarity is enforced both spatially and temporally.  assume that the prior on the swarm dynamics is distributed according to an MRF in both space and time. Embedding this model in a MAP framework,  iterate beten learning the spatial layout of the swarm and its dynamics.  learn the swarm transformations using ICM, which iterates beten estimating these transformations and updating their distribution in the spatiotemporal neighborhoods.  demonstrate the validity of our method by conducting experiments on real and synthetic video sequences. Real sequences of birds, geese, robot swarms, and pedestrians evaluate the applicability of our model to real world data."}]