TY - JOUR
T1 - Latent Body-Pose guided DenseNet for Recognizing Driver’s Fine-grained Secondary Activities
AU - Behera, Ardhendu
AU - Keidel, Alex
N1 - 15th IEEE International Conference on Advanced Video and Signal-based Surveillance (AVSS) 27th - 30th November 2018 Auckland, New Zealand
PY - 2019/2/14
Y1 - 2019/2/14
N2 - Over the past two decades, there has been an increasing research in developing self-driving vehicles, with many industries pushing the bounds alongside academia. Automatic recognition of in-vehicle activities plays a key role in developing such vehicles. In this work, we propose a novel human-pose driven approach for video-based monitoring of driver’s state/activity and is inspired by the recent success of deep Convolutional Neural Network (CNN) in visual recognition tasks. The approach infers the driver’s state/activity from a single frame and thus, could operate in real-time. We also bring together ideas from recent works
on human pose detection and transfer learning for visual recognition. The adapted DenseNet integrates these ideas under one framework, where one stream is focused on the
latent body pose and the other stream is on appearance information. The proposed method is extensively evaluated on two challenging datasets consisting various secondary nondriving activities. Our experimental results demonstrate that the driver activity recognition performance improves significantly when the latent body-pose is integrated into the existing deep networks.
AB - Over the past two decades, there has been an increasing research in developing self-driving vehicles, with many industries pushing the bounds alongside academia. Automatic recognition of in-vehicle activities plays a key role in developing such vehicles. In this work, we propose a novel human-pose driven approach for video-based monitoring of driver’s state/activity and is inspired by the recent success of deep Convolutional Neural Network (CNN) in visual recognition tasks. The approach infers the driver’s state/activity from a single frame and thus, could operate in real-time. We also bring together ideas from recent works
on human pose detection and transfer learning for visual recognition. The adapted DenseNet integrates these ideas under one framework, where one stream is focused on the
latent body pose and the other stream is on appearance information. The proposed method is extensively evaluated on two challenging datasets consisting various secondary nondriving activities. Our experimental results demonstrate that the driver activity recognition performance improves significantly when the latent body-pose is integrated into the existing deep networks.
KW - Deep Learning
KW - Transfer Learning
KW - Autonomous Vehicles
KW - In-vehicle Activity Monitoring
KW - Body pose
KW - DenseNet
KW - Drivers distractions recognition
UR - https://avss2018.org/program
UR - https://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001307
M3 - Article (journal)
JO - 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
JF - 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)
ER -