Measuring shape and motion of a high-speed object with designed features from motion blurred images. (October 2019)

• Record Type: Journal Article
• Authors: Zhou, Hance; Chen, Mingjun; Zhang, Liyan; Ye, Nan; Tao, Cong
• Is Part Of: Measurement. Volume 145(2019)
• Page Start: 559
• Page End: 567
• Publication Date: 2019-10
• Subjects: Motion blur -- Vision based measurement -- Inner-frame path -- Trans-frame path -- Blurred targets recognition -- 3D reconstruction; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.05.023
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A convolutional neural network (CNN) based method for recognizing motion blurred visual targets was proposed. A motion blur model based on inner-frame path superposition imaging was established. An automatic blur image synthesizing method was put forward for generating the large amount of training data of the CNN. An optimization framework was set up to reconstruct the 3D target moving path during camera exposure. Abstract: Vision-based geometry measurement plays a crucial role in many science and industrial areas. Plenty of researches devoted to measuring static objects, while few focused on motion blurred situations, which inevitably arise when the object being measured moves fast relative to the camera(s). Motion blur usually invalids the vision-based measurement algorithms designated for static objects. In this paper, we devote to accurate three dimensional (3D) reconstruction of moving objects from motion blurred stereo image pairs. A convolutional neural network (CNN) based method is first proposed to recognize the motion blurred visual targets. A motion blur model based on inner-frame path superposition imaging is then established. Finally, an optimization framework is set up to reconstruct the 3D target motion path during the camera exposure. Experiments are involved to demonstrate the validity and accuracy of the method.