Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a method of identifying persons who are using wheelchairs in real environments on the basis of HOG features extracted from disparity images. First,  use USV, which is a stereo vision system for detecting humans, to determine the 3-D location of the humans moving within the monitored area, and cut out the corresponding regions in the disparity image. Then,  compute a HOG feature vector from those cut-out images, divide the results into the two classes of persons in wheelchairs and pedestrians and perform training and distinguishing by SVM. The data dealt with here involved people moving around without restriction in real environments. Also, there was no guarantee that the photographed environment was the same at the time of training data acquisition and at the time of recognition processing.  conducted training and recognition experiments on this method using video data acquired in the laboratory that confirmed a per-frame recognition rate of 99% or higher. Furthermore, after training with the same laboratory data and application to video data of visitors to an assistive products exhibition, a per-frame correct recognition rate of 80% was confirmed.
['Daisuke Hosotani', 'Ikushi Yoda', 'Katsuhiko Sakaue']
I am writing a research paper on a idea Wheelchair recognition by using stereo vision and histogram of oriented gradients (HOG) in real environments
[{"from": "human", "value": "I am writing a research paper on a idea Wheelchair recognition by using stereo vision and histogram of oriented gradients (HOG) in real environments"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  propose a method of identifying persons who are using wheelchairs in real environments on the basis of HOG features extracted from disparity images. First,  use USV, which is a stereo vision system for detecting humans, to determine the 3-D location of the humans moving within the monitored area, and cut out the corresponding regions in the disparity image. Then,  compute a HOG feature vector from those cut-out images, divide the results into the two classes of persons in wheelchairs and pedestrians and perform training and distinguishing by SVM. The data dealt with here involved people moving around without restriction in real environments. Also, there was no guarantee that the photographed environment was the same at the time of training data acquisition and at the time of recognition processing.  conducted training and recognition experiments on this method using video data acquired in the laboratory that confirmed a per-frame recognition rate of 99% or higher. Furthermore, after training with the same laboratory data and application to video data of visitors to an assistive products exhibition, a per-frame correct recognition rate of 80% was confirmed."}]