首页|Findings in Intelligent and Connected Vehicles Reported from Tokyo Institute of Technology (Scale variant vehicle object recognition by CNN module of multi-pool ing-PCA process)
Findings in Intelligent and Connected Vehicles Reported from Tokyo Institute of Technology (Scale variant vehicle object recognition by CNN module of multi-pool ing-PCA process)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on intelligent and connected ve hicles are discussed in a new report. According to news reporting out of Tokyo, Japan, by NewsRx editors, research stated, "The moving vehicles present differen t scales in the image due to the perspective effect of different viewpoint dista nces." Funders for this research include National Natural Science Foundation of China. The news reporters obtained a quote from the research from Tokyo Institute of Te chnology: "The premise of advanced driver assistance system (ADAS) system for sa fety surveillance and safe driving is early identification of vehicle targets in front of the ego vehicle. The recognition of the same vehicle at different scal es requires feature learning with scale invariance. Unlike existing feature vect or methods, the normalized PCA eigenvalues calculated from feature maps are used to extract scale-invariant features. This study proposed a convolutional neural network (CNN) structure embedded with the module of multipooling- PCA for scale variant object recognition. The validation of the proposed network structure is verified by scale variant vehicle image dataset. Compared with scale invariant network algorithms of Scaleinvariant feature transform (SIFT) and FSAF as well as miscellaneous networks, the proposed network can achieve the best recognition accuracy tested by the vehicle scale variant dataset."
Tokyo Institute of TechnologyTokyoJa panAsiaIntelligent and Connected VehiclesTransportation
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