Monocular Vision Ranging and Camera Focal Length Calibration

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Abstract

The camera calibration in monocular vision represents the relationship between the pixels’ units which is obtained from a camera and the object in the real world. As an essential procedure, camera calibration calculates the three-dimensional geometric information from the captured two-dimensional images. Therefore, a modified camera calibration method based on polynomial regression is proposed to simplify. In this method, a parameter vector is obtained by pixel coordinates of obstacles and corresponding distance values using polynomial regression. The set of parameter’s vectors can measure the distance between the camera and the ground object in the field of vision under the camera’s posture and position. The experimental results show that the lowest accuracy of this focal length calibration method for measurement is 97.09%, and the average accuracy was 99.02%.

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

Shaoqing Ren, Kaiming He, Ross Girshick … (2017)

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3], our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.