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• W2840753721 abstract "Automated cell phone usage detection systems are a privilege for traffic law enforcement to avoid increasing death toll due to irresponsible drive behavior This paper presents a computer vision based approach to automatically monitor driver's behavior during driving especially focusing on irresponsible behavior like use of mobile phone. Majority of the earlier work is based on the camera system inside the vehicle. Our framework however, is based on traffic monitoring cameras on the road. At first, we learn deep action specific features based on pose and appearance representation and train an SVM, named deep actionlet. We then use action-let activations from input images to get candidate actionlet proposals. Finally, we integrate these proposals into a Faster spectral-RCNN replacing region proposal network. The spectral domain provides a significant speedup with underlying model unchanged. The proposed approach is evaluated on the challenging Strategic Highway Research Program (SHRP2) and internally developed Driver in the Wild dataset. Detailed experimental results show that our approach achieves better performance compared to the state-of-the art approaches." @default.
• W2840753721 created "2018-07-19" @default.
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• W2840753721 date "2017-12-01" @default.
• W2840753721 modified "2023-09-28" @default.
• W2840753721 title "Deep actionlet proposals for driver's behavior monitoring" @default.
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• W2840753721 doi "https://doi.org/10.1109/ivcnz.2017.8402447" @default.
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