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EMGNet: Driver’s Intent Prediction using Neurosignals
Shoaib Azam,Farzeen Munir,Unse Fatima,Moongu Jeon 제어로봇시스템학회 2021 제어로봇시스템학회 국내학술대회 논문집 Vol.2021 No.6
Many traffic accidents happen because of the negligence of the human drivers. The recent development in sensor technology provide a way to understand the human intent while driving. In this work, we have developed a neural network EMGNet that utilizes the electromyography (EMG) signals fused with inertial measurement unit (IMU) data from a wearable sensor placed on the driver forearm to predict the driver intention prior to actual movement of steering wheel. The experimental evaluation shows 76 % accuracy in predicting the driver’s intention with 1600 µs earlier the actual movement on the steering wheel.
Transfer learning for vehicle detection using two cameras with different focal lengths
Quang Dinh, Vinh,Munir, Farzeen,Azam, Shoaib,Yow, Kin-Choong,Jeon, Moongu Elsevier science 2020 Information sciences Vol.514 No.-
<P><B>Abstract</B></P> <P>This paper proposes a vehicle detection method using transfer learning for two cameras with different focal lengths. A detected vehicle region in an image of one camera is transformed into a binary map. After that, the map is used to filter convolutional neural network (CNN) feature maps which are computed for the other camera’s image. We also introduce a robust evolutionary algorithm that is used to compute the relationship between the two cameras in an off-line mode efficiently. We capture video sequences and sample them to make a dataset that includes images with different focal lengths for vehicle detection. We compare the proposed vehicle detection method with baseline detection methods, including faster region proposal networks (Faster-RCNN), single-shot-multi-Box detector (SSD), and detector using recurrent rolling convolution (RRC), in the same experimental context. The experimental results show that the proposed method can detect vehicles at a wide range of distances accurately and robustly, and significantly outperforms the baseline detection methods.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A transfer learning based vehicle detection method is proposed for two cameras with different focal lengths and effectively solves tiny object size problems. </LI> <LI> REAL is introduced to compute relationship between two cameras effectively. </LI> <LI> Vehicle datasets using cameras with different focal lengths were captured and labeled to evaluate vehicle detection methods. Experimental results show the proposed vehicle detection method significantly outperforms baseline methods. </LI> </UL> </P>