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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.
Tzu-Yi Chuang; Jen-Yu Han; Deng-Jie Jhan; Ming-Der Yang. Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN. Remote Sensing 2020, 12, 1 .
AMA StyleTzu-Yi Chuang, Jen-Yu Han, Deng-Jie Jhan, Ming-Der Yang. Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN. Remote Sensing. 2020; 12 (12):1.
Chicago/Turabian StyleTzu-Yi Chuang; Jen-Yu Han; Deng-Jie Jhan; Ming-Der Yang. 2020. "Geometric Recognition of Moving Objects in Monocular Rotating Imagery Using Faster R-CNN." Remote Sensing 12, no. 12: 1.
Routine maintenance of drainage systems, including structure inspection and dredging, plays an essential role in disaster prevention and reduction. Autonomous systems have been explored to assist in pipeline inspection due to safety issues in unknown underground environments. Most of the existing systems merely rely on video records for visual examination since sensors such as a laser scanner or sonar are costly, and the data processing requires expertise. This study developed a compact platform for sewer inspection, which consisted of low-cost components such as infrared and depth cameras with a g-sensor. Except for visual inspection, the platform not only identifies internal faults and obstacles but also evaluates their geometric information, geo-locations, and the block ratio of a pipeline in an automated fashion. As the platform moving, the g-sensor reflects the pipeline flatness, while an integrated simultaneous localization and mapping (SLAM) strategy reconstructs the 3D map of the pipeline conditions simultaneously. In the light of the experimental results, the reconstructed moving trajectory achieved a relative accuracy of 0.016 m when no additional control points deployed along the inspecting path. The geometric information of observed defects accomplishes an accuracy of 0.9 cm in length and width estimation and an accuracy of 1.1% in block ratio evaluation, showing promising results for practical sewer inspection. Moreover, the labeled deficiencies directly increase the automation level of documenting irregularity and facilitate the understanding of pipeline conditions for management and maintenance.
Tzu-Yi Chuang; Cheng-Che Sung. Learning and SLAM Based Decision Support Platform for Sewer Inspection. Remote Sensing 2020, 12, 968 .
AMA StyleTzu-Yi Chuang, Cheng-Che Sung. Learning and SLAM Based Decision Support Platform for Sewer Inspection. Remote Sensing. 2020; 12 (6):968.
Chicago/Turabian StyleTzu-Yi Chuang; Cheng-Che Sung. 2020. "Learning and SLAM Based Decision Support Platform for Sewer Inspection." Remote Sensing 12, no. 6: 968.
Geospatial and geometric states of traffic signs are indispensable information for traffic facility maintenance. This study applies a low-cost single camera system to locate and identify traffic signs and reflects their on-site conditions for assisting in maintenance. Referring to official regulations, a traffic sign can be identified and classified based on its color and shape attributes. The poses and states of the sign planes can also be evaluated according to their geometric shape ratios and the discrepancy with respect to traffic sign regulations, in which a least-square consistency check is proposed to ensure assessment reliability and accuracy. Validation with day and nighttime image sequences was performed to verify the effectiveness of the proposed method and the robustness to illumination changes, color deterioration, and motion blur. Considering the promising results, this study can be deemed as an alternative to promote routine maintenance of traffic facilities.
Jen-Yu Han; Tsung-Hsien Juan; Tzu-Yi Chuang. Traffic sign detection and positioning based on monocular camera. Journal of the Chinese Institute of Engineers 2019, 42, 757 -769.
AMA StyleJen-Yu Han, Tsung-Hsien Juan, Tzu-Yi Chuang. Traffic sign detection and positioning based on monocular camera. Journal of the Chinese Institute of Engineers. 2019; 42 (8):757-769.
Chicago/Turabian StyleJen-Yu Han; Tsung-Hsien Juan; Tzu-Yi Chuang. 2019. "Traffic sign detection and positioning based on monocular camera." Journal of the Chinese Institute of Engineers 42, no. 8: 757-769.
Light detection and ranging (LiDAR) has become a mainstream technique for rapid acquisition of 3-D geometry. Current LiDAR platforms can be mainly categorized into spaceborne LiDAR system (SLS), airborne LiDAR system (ALS), mobile LiDAR system (MLS), and terrestrial LiDAR system (TLS). Point cloud registration between different scans of the same platform or different platforms is essential for establishing a complete scene description and improving geometric consistency. The discrepancies in data characteristics should be manipulated properly for precise transformation estimation. This paper proposes a multi-feature registration scheme suitable for utilizing point, line, and plane features extracted from raw point clouds to realize the registrations of scans acquired within the same LIDAR system or across the different platforms. By exploiting the full geometric strength of the features, different features are used exclusively or combined with others. The uncertainty of feature observations is also considered within the proposed method, in which the registration of multiple scans can be simultaneously achieved. The simulated test with an ideal geometry and data simplification was performed to assess the contribution of different features towards point cloud registration in a very essential fashion. On the other hand, three real cases of registration between LIDAR scans from single platform and between those acquired by different platforms were demonstrated to validate the effectiveness of the proposed method. In light of the experimental results, it was found that the proposed model with simultaneous and weighted adjustment rendered satisfactory registration results and showed that not only features inherited in the scene can be more exploited to increase the robustness and reliability for transformation estimation, but also the weak geometry of poorly overlapping scans can be better treated than utilizing only one single type of feature. The registration errors of multiple scans in all tests were all less than point interval or positional error, whichever dominating, of the LiDAR data.
Tzu-Yi Chuang; Jen-Jer Jaw. Multi-Feature Registration of Point Clouds. Remote Sensing 2017, 9, 281 .
AMA StyleTzu-Yi Chuang, Jen-Jer Jaw. Multi-Feature Registration of Point Clouds. Remote Sensing. 2017; 9 (3):281.
Chicago/Turabian StyleTzu-Yi Chuang; Jen-Jer Jaw. 2017. "Multi-Feature Registration of Point Clouds." Remote Sensing 9, no. 3: 281.