Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people,  propose the use of the abstract hidden Markov model (AHMM), which can be considered as an extension of the hidden Markov model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackllised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy. The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. Results of the system demonstrate the ability to ansr queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment.
['Nam Thanh Nguyen', 'Svetha Venkatesh', 'Geoff A. W. West', 'Hung Hai Bui']
I am writing a research paper on a idea Hierarchical monitoring of people's behaviors in complex environments using multiple cameras
[{"from": "human", "value": "I am writing a research paper on a idea Hierarchical monitoring of people's behaviors in complex environments using multiple cameras"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people,  propose the use of the abstract hidden Markov model (AHMM), which can be considered as an extension of the hidden Markov model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackllised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy. The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. Results of the system demonstrate the ability to ansr queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment."}]