Browsing by Author "Santoso, Patrisia Sherryl"

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LEARNING-BASED HUMAN DETECTION APPLIED TO RGB-D IMAGES
(2017-01-01)
Santoso, Patrisia Sherryl
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Hang, Hsueh-Ming
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資訊工程學系
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Department of Computer Science
Accurate human detection is still a challenging topic due to complicated environments in the real world. In addition, the RGB-D cameras are becoming popular at reasonable price, such as Microsoft Kinect sensor, which provides both RGB and depth data. The depth information often helpful for detection. We adopt the R-CNN method in this paper, which combines the Selective Search technique to generate region proposals and the CNNs (Convolutional Neural Networks) to learn features. A depth map encoding technique (HHA) is adopted to match the CNNs format for learning features. The HHA and RGB images are our inputs. We propose several algorithms to combine their information in constructing various human detectors. Our information fusion structures include CNN, SVM together with PCA for features reduction. More accurate human detection results are shown with the aid of depth information.
One Stage Detection Network with an Auxiliary Classifier for Real-Time Road Marks Detection
(2018-01-01)
Lin, Guan-Ting
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Santoso, Patrisia Sherryl
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Lin, Che-Tsung
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Tsai, Chia-Chi
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Guo, Jiun-In
We construct a robust road mark detector that achieves high accuracy with real-time processing performance ( 32 fps) under nVidia Titan-X GPU. We combine one stage deep learning detector with auxiliary CNN classifiers as a robust road marks detector. We found out that one stage detector not only detects multiple objects via single inference efficiently, but also remains a good accuracy in performance perspective. However, to make it better, we add an extra CNN classifier as the back part of the proposed architecture to reduce false positive and get better accuracy. The proposed detector can achieve 86.8% mAP in our in-house six-class road mark database.
Stop Line Detection and Distance Measurement for Road Intersection based on. Deep Learning Neural Network
(2017-01-01)
Lin, Guan-Ting
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Santoso, Patrisia Sherryl
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Lin, Che-Tsung
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Tsai, Chia-Chi
;
Guo, Jiun-In
In this paper, we introduce Boost-CNN, a robust stop-line detector that can detect objects (stop line) with competitive tradeoff between speed and accuracy. Boost-CNN consists of an AdaBoost classifier and a CNN. The former is our region proposal generator and it is further combined with the later to be a stop-line detector. In addition, an automatic hard mining method is proposed to reduce the number of false alarm. Our proposed detector achieves 91.5% in accuracy and has 100 FPS performance in test time (performed on NVITAA DIGITS DevBox and Titan X GPU).