Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel approach to single-frame pedestrian classification and orientation estimation. Unlike previous work which addressed classification and orientation separately with different models, our method involves a probabilistic framework to approach both in a unified fashion.  address both problems in terms of a set of view-related models which couple discriminative expert classifiers with sample-dependent priors, facilitating easy integration of other cues (e.g. motion, shape) in a Bayesian fashion. This mixture-of-experts formulation approximates the probability density of pedestrian orientation and scales-up to the use of multiple cameras. Experiments on large real-world data show a significant performance improvement in both pedestrian classification and orientation estimation of up to 50%, compared to state-of-the-art, using identical data and evaluation techniques.
['Markus Enzweiler', 'Dariu M. Gavrila']
I am writing a research paper on a idea Integrated pedestrian classification and orientation estimation
[{"from": "human", "value": "I am writing a research paper on a idea Integrated pedestrian classification and orientation estimation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel approach to single-frame pedestrian classification and orientation estimation. Unlike previous work which addressed classification and orientation separately with different models, our method involves a probabilistic framework to approach both in a unified fashion.  address both problems in terms of a set of view-related models which couple discriminative expert classifiers with sample-dependent priors, facilitating easy integration of other cues (e.g. motion, shape) in a Bayesian fashion. This mixture-of-experts formulation approximates the probability density of pedestrian orientation and scales-up to the use of multiple cameras. Experiments on large real-world data show a significant performance improvement in both pedestrian classification and orientation estimation of up to 50%, compared to state-of-the-art, using identical data and evaluation techniques."}]