Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Notwithstanding many years of progress, pedestrian recognition is still a difficult but important problem.  present a novel multilevel Mixture-of-Experts approach to combine information from multiple features and cues with the objective of improved pedestrian classification. On pose-level, shape cues based on Chamfer shape matching provide sample-dependent priors for a certain pedestrian view. On modality-level,  represent each data sample in terms of image intensity, (dense) depth, and (dense) flow. On feature-level,  consider histograms of oriented gradients (HOG) and local binary patterns (LBP). Multilayer perceptrons (MLP) and linear support vector machines (linSVM) are used as expert classifiers. Experiments are performed on a unique real-world multi-modality dataset captured from a moving vehicle in urban traffic. This dataset has been made public for research purposes. Our results show a significant performance boost of up to a factor of 42 in reduction of false positives at constant detection rates of our approach compared to a baseline intensity-only HOG/linSVM approach.
['Markus Enzweiler', 'Dariu M. Gavrila']
I am writing a research paper on a idea A Multilevel Mixture-of-Experts Framework for Pedestrian Classification
[{"from": "human", "value": "I am writing a research paper on a idea A Multilevel Mixture-of-Experts Framework for Pedestrian Classification"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Notwithstanding many years of progress, pedestrian recognition is still a difficult but important problem.  present a novel multilevel Mixture-of-Experts approach to combine information from multiple features and cues with the objective of improved pedestrian classification. On pose-level, shape cues based on Chamfer shape matching provide sample-dependent priors for a certain pedestrian view. On modality-level,  represent each data sample in terms of image intensity, (dense) depth, and (dense) flow. On feature-level,  consider histograms of oriented gradients (HOG) and local binary patterns (LBP). Multilayer perceptrons (MLP) and linear support vector machines (linSVM) are used as expert classifiers. Experiments are performed on a unique real-world multi-modality dataset captured from a moving vehicle in urban traffic. This dataset has been made public for research purposes. Our results show a significant performance boost of up to a factor of 42 in reduction of false positives at constant detection rates of our approach compared to a baseline intensity-only HOG/linSVM approach."}]