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Article

Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN

1
Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan
2
Department of Civil Engineering, National Taiwan University, Taipei 106, Taiwan
3
Land Administration Department, Yunlin County Government, Yunlin 640, Taiwan
4
Department of Civil Engineering, and Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 402, Taiwan
5
Pervasive AI Research (PAIR) Labs, Hsinchu 300, Taiwan
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 1908; https://doi.org/10.3390/rs12121908
Received: 1 May 2020 / Revised: 8 June 2020 / Accepted: 8 June 2020 / Published: 12 June 2020
Moving object detection and tracking from image sequences has been extensively studied in a variety of fields. Nevertheless, observing geometric attributes and identifying the detected objects for further investigation of moving behavior has drawn less attention. The focus of this study is to determine moving trajectories, object heights, and object recognition using a monocular camera configuration. This paper presents a scheme to conduct moving object recognition with three-dimensional (3D) observation using faster region-based convolutional neural network (Faster R-CNN) with a stationary and rotating Pan Tilt Zoom (PTZ) camera and close-range photogrammetry. The camera motion effects are first eliminated to detect objects that contain actual movement, and a moving object recognition process is employed to recognize the object classes and to facilitate the estimation of their geometric attributes. Thus, this information can further contribute to the investigation of object moving behavior. To evaluate the effectiveness of the proposed scheme quantitatively, first, an experiment with indoor synthetic configuration is conducted, then, outdoor real-life data are used to verify the feasibility based on recall, precision, and F1 index. The experiments have shown promising results and have verified the effectiveness of the proposed method in both laboratory and real environments. The proposed approach calculates the height and speed estimates of the recognized moving objects, including pedestrians and vehicles, and shows promising results with acceptable errors and application potential through existing PTZ camera images at a very low cost. View Full-Text
Keywords: video surveillance; Faster R-CNN; object recognition; deep learning video surveillance; Faster R-CNN; object recognition; deep learning
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MDPI and ACS Style

Chuang, T.-Y.; Han, J.-Y.; Jhan, D.-J.; Yang, M.-D. Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN. Remote Sens. 2020, 12, 1908. https://doi.org/10.3390/rs12121908

AMA Style

Chuang T-Y, Han J-Y, Jhan D-J, Yang M-D. Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN. Remote Sensing. 2020; 12(12):1908. https://doi.org/10.3390/rs12121908

Chicago/Turabian Style

Chuang, Tzu-Yi, Jen-Yu Han, Deng-Jie Jhan, and Ming-Der Yang. 2020. "Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN" Remote Sensing 12, no. 12: 1908. https://doi.org/10.3390/rs12121908

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