scholarly journals An Approach for Moving Object Recognition Based on BPR and CI

10.5772/15742 ◽  
2011 ◽  
Author(s):  
Li Wang ◽  
Lida Xu ◽  
Renjing Liu ◽  
Hai Hong
Keyword(s):  
Object Recognition ◽  
Moving Object

SiMOR: Single Moving Object Recognition

2011 ◽  
Vol 20 (1) ◽  
Author(s):  
M. T. Gopala Krishna ◽  
M. Ravishankar ◽  
D. R. Ramesh Babu ◽  
V. N. Manjunath Aradhya
Keyword(s):  
Object Recognition ◽  
Moving Object

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

2020 ◽  
Vol 12 (12) ◽  
pp. 1908
Author(s):  
Tzu-Yi Chuang ◽  
Jen-Yu Han ◽  
Deng-Jie Jhan ◽  
Ming-Der Yang
Keyword(s):  
Object Recognition ◽  
Moving Objects ◽  
Low Cost ◽  
Three Dimensional ◽  
Real Life ◽  
Moving Object ◽  
Camera Motion ◽  
Ptz Camera ◽  
Real Life Data ◽  
Close Range

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.

Moving object recognition by a shape-based neural fuzzy network

2008 ◽  
Vol 71 (13-15) ◽  
pp. 2937-2949 ◽  
Author(s):  
Chia-Feng Juang ◽  
Liang-Tso Chen
Keyword(s):  
Object Recognition ◽  
Moving Object ◽  
Fuzzy Network ◽  
Neural Fuzzy Network ◽  
Neural Fuzzy
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