20,531 research outputs found
Detecting Distracted Driving with Deep Learning
© Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.Peer reviewe
The Evolution of First Person Vision Methods: A Survey
The emergence of new wearable technologies such as action cameras and
smart-glasses has increased the interest of computer vision scientists in the
First Person perspective. Nowadays, this field is attracting attention and
investments of companies aiming to develop commercial devices with First Person
Vision recording capabilities. Due to this interest, an increasing demand of
methods to process these videos, possibly in real-time, is expected. Current
approaches present a particular combinations of different image features and
quantitative methods to accomplish specific objectives like object detection,
activity recognition, user machine interaction and so on. This paper summarizes
the evolution of the state of the art in First Person Vision video analysis
between 1997 and 2014, highlighting, among others, most commonly used features,
methods, challenges and opportunities within the field.Comment: First Person Vision, Egocentric Vision, Wearable Devices, Smart
Glasses, Computer Vision, Video Analytics, Human-machine Interactio
On validating a generic camera-based blink detection system for cognitive load assessment
Detecting the human operator\u27s cognitive state is paramount in settings wherein maintaining optimal workload is necessary for task performance. Blink rate is an established metric of cognitive load, with a higher blink frequency being observed under conditions of greater workload. Measuring blink rate requires the use of eye-trackers which limits the adoption of this metric in the real-world. The authors aim to investigate the effectiveness of using a generic camera-based system as a way to assess the user\u27s cognitive load during a computer task. Participants completed a mental task while sitting in front of a computer. Blink rate was recorded via both the generic camera-based system and a scientific-grade eye-tracker for validation purposes. Cognitive load was also assessed through the performance in a single stimulus detection task. The blink rate recorded via the generic camera-based approach did not differ from the one obtained through the eye-tracker. No meaningful changes in blink rate were however observed with increasing cognitive load. Results show the generic-camera based system may represent a more affordable, ubiquitous means for assessing cognitive workload during computer task. Future work should further investigate ways to increase its accuracy during the completion of more realistic tasks
A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts
This paper presents a multi-robot system for manufacturing personalized
medical stent grafts. The proposed system adopts a modular design, which
includes: a (personalized) mandrel module, a bimanual sewing module, and a
vision module. The mandrel module incorporates the personalized geometry of
patients, while the bimanual sewing module adopts a learning-by-demonstration
approach to transfer human hand-sewing skills to the robots. The human
demonstrations were firstly observed by the vision module and then encoded
using a statistical model to generate the reference motion trajectories. During
autonomous robot sewing, the vision module plays the role of coordinating
multi-robot collaboration. Experiment results show that the robots can adapt to
generalized stent designs. The proposed system can also be used for other
manipulation tasks, especially for flexible production of customized products
and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial
Informatics, Key words: modularity, medical device customization, multi-robot
system, robot learning, visual servoing, robot sewin
A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts
This paper presents a multi-robot system for manufacturing personalized
medical stent grafts. The proposed system adopts a modular design, which
includes: a (personalized) mandrel module, a bimanual sewing module, and a
vision module. The mandrel module incorporates the personalized geometry of
patients, while the bimanual sewing module adopts a learning-by-demonstration
approach to transfer human hand-sewing skills to the robots. The human
demonstrations were firstly observed by the vision module and then encoded
using a statistical model to generate the reference motion trajectories. During
autonomous robot sewing, the vision module plays the role of coordinating
multi-robot collaboration. Experiment results show that the robots can adapt to
generalized stent designs. The proposed system can also be used for other
manipulation tasks, especially for flexible production of customized products
and where bimanual or multi-robot cooperation is required.Comment: 10 pages, 12 figures, accepted by IEEE Transactions on Industrial
Informatics, Key words: modularity, medical device customization, multi-robot
system, robot learning, visual servoing, robot sewin
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