The images from a monocular camera can be processed to detect depth information regarding obstacles in the blind spot area captured by the side-view camera of a vehicle.The depth information is given as a classificati...The images from a monocular camera can be processed to detect depth information regarding obstacles in the blind spot area captured by the side-view camera of a vehicle.The depth information is given as a classification result“near”or“far”when two blocks in the image are compared with respect to their distances and the depth information can be used for the purpose of blind spot area detection.In this paper,the proposed depth information is inferred from a combination of blur cues and texture cues.The depth information is estimated by comparing the features of two image blocks selected within a single image.A preliminary experiment demonstrates that a convolutional neural network(CNN)model trained by deep learning with a set of relatively ideal images achieves good accuracy.The same CNN model is applied to distinguish near and far obstacles according to a specified threshold in the vehicle blind spot area,and the promising results are obtained.The proposed method uses a standard blind spot camera and can improve safety without other additional sensing devices.Thus,the proposed approach has the potential to be applied in vehicular applications for the detection of objects in the driver’s blind spot.展开更多
The moving vehicles present different scales in the image due to the perspective effect of different viewpoint distances.The premise of advanced driver assistance system(ADAS)system for safety surveillance and safe dr...The moving vehicles present different scales in the image due to the perspective effect of different viewpoint distances.The premise of advanced driver assistance system(ADAS)system for safety surveillance and safe driving is early identification of vehicle targets in front of the ego vehicle.The recognition of the same vehicle at different scales requires feature learning with scale invariance.Unlike existing feature vector 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 multi-pooling-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 Scale-invariant 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.To testify the practicality of this modified network,the testing of public dataset ImageNet is done and the comparable results proved its effectiveness in general purpose of applications.展开更多
文摘The images from a monocular camera can be processed to detect depth information regarding obstacles in the blind spot area captured by the side-view camera of a vehicle.The depth information is given as a classification result“near”or“far”when two blocks in the image are compared with respect to their distances and the depth information can be used for the purpose of blind spot area detection.In this paper,the proposed depth information is inferred from a combination of blur cues and texture cues.The depth information is estimated by comparing the features of two image blocks selected within a single image.A preliminary experiment demonstrates that a convolutional neural network(CNN)model trained by deep learning with a set of relatively ideal images achieves good accuracy.The same CNN model is applied to distinguish near and far obstacles according to a specified threshold in the vehicle blind spot area,and the promising results are obtained.The proposed method uses a standard blind spot camera and can improve safety without other additional sensing devices.Thus,the proposed approach has the potential to be applied in vehicular applications for the detection of objects in the driver’s blind spot.
基金supported by the National Natural Science Foundation of China(Grant No.51875340).
文摘The moving vehicles present different scales in the image due to the perspective effect of different viewpoint distances.The premise of advanced driver assistance system(ADAS)system for safety surveillance and safe driving is early identification of vehicle targets in front of the ego vehicle.The recognition of the same vehicle at different scales requires feature learning with scale invariance.Unlike existing feature vector 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 multi-pooling-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 Scale-invariant 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.To testify the practicality of this modified network,the testing of public dataset ImageNet is done and the comparable results proved its effectiveness in general purpose of applications.
