scholarly journals A Novel Method of Object Detection from a Moving Camera Based on Image Matching and Frame Coupling

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e109809 ◽  
Author(s):  
Yong Chen ◽  
Rong hua Zhang ◽  
Lei Shang
Keyword(s):  
Object Detection ◽  
Image Matching ◽  
Moving Camera ◽  
Novel Method

The research of moving object detection with a moving camera

2013 ◽  
Author(s):  
Hai-xin Chen ◽  
Guo-hua Gu ◽  
Xiao-feng Bai ◽  
Tie-kun Zhao ◽  
Fu-yuan Xu
Keyword(s):  
Object Detection ◽  
Moving Object Detection ◽  
Moving Object ◽  
Moving Camera

scholarly journals UNSUPERVISED MULTI-CONSTRAINT DEEP NEURAL NETWORK FOR DENSE IMAGE MATCHING

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 163-167
Author(s):  
W. Yuan ◽  
Z. Fan ◽  
X. Yuan ◽  
J. Gong ◽  
R. Shibasaki
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Image Matching ◽  
Deep Neural Network ◽  
Aerial Images ◽  
Surface Model ◽  
Learning Technology ◽  
Ground Sample ◽  
Volume Calculation ◽  
Dense Image

Abstract. Dense image matching is essential to photogrammetry applications, including Digital Surface Model (DSM) generation, three dimensional (3D) reconstruction, and object detection and recognition. The development of an efficient and robust method for dense image matching has been one of the technical challenges due to high variations in illumination and ground features of aerial images of large areas. Nowadays, due to the development of deep learning technology, deep neural network-based algorithms outperform traditional methods on a variety of tasks such as object detection, semantic segmentation and stereo matching. The proposed network includes cost-volume computation, cost-volume aggregation, and disparity prediction. It starts with a pre-trained VGG-16 network as a backend and using the U-net architecture with nine layers for feature map extraction and a correlation layer for cost volume calculation, after that a guided filter based cost aggregation is adopted for cost volume filtering and finally the soft Argmax function is utilized for disparity prediction. The experimental conducted on a UAV dataset demonstrated that the proposed method achieved the RMSE (root mean square error) of the reprojection error better than 1 pixel in image coordinate and in-ground positioning accuracy within 2.5 ground sample distance. The comparison experiments on KITTI 2015 dataset shows the proposed unsupervised method even comparably with other supervised methods.

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