7 research outputs found
Towards Real-Time Head Pose Estimation: Exploring Parameter-Reduced Residual Networks on In-the-wild Datasets
Head poses are a key component of human bodily communication and thus a
decisive element of human-computer interaction. Real-time head pose estimation
is crucial in the context of human-robot interaction or driver assistance
systems. The most promising approaches for head pose estimation are based on
Convolutional Neural Networks (CNNs). However, CNN models are often too complex
to achieve real-time performance. To face this challenge, we explore a popular
subgroup of CNNs, the Residual Networks (ResNets) and modify them in order to
reduce their number of parameters. The ResNets are modifed for different image
sizes including low-resolution images and combined with a varying number of
layers. They are trained on in-the-wild datasets to ensure real-world
applicability. As a result, we demonstrate that the performance of the ResNets
can be maintained while reducing the number of parameters. The modified ResNets
achieve state-of-the-art accuracy and provide fast inference for real-time
applicability.Comment: 32nd International Conference on Industrial, Engineering & Other
Applications of Applied Intelligent Systems (IEA/AIE 2019
Understanding social relationships in egocentric vision
The understanding of mutual people interaction is a key component for recognizing people social behavior, but it strongly relies on a personal point of view resulting difficult to be a-priori modeled. We propose the adoption of the unique head mounted cameras first person perspective (ego-vision) to promptly detect people interaction in different social contexts. The proposal relies on a complete and reliable system that extracts people\u5f3s head pose combining landmarks and shape descriptors in a temporal smoothed HMM framework. Finally, interactions are detected through supervised clustering on mutual head orientation and people distances exploiting a structural learning framework that specifically adjusts the clustering measure according to a peculiar scenario. Our solution provides the flexibility to capture the interactions disregarding the number of individuals involved and their level of acquaintance in context with a variable degree of social involvement. The proposed system shows competitive performances on both publicly available ego-vision datasets and ad hoc benchmarks built with real life situations
A Developmental Model of Trust in Humanoid Robots
Trust between humans and artificial systems has recently received increased attention due to the widespread use of autonomous systems in our society. In this context trust plays a dual role. On the one hand it is necessary to build robots that are perceived as trustworthy by humans. On the other hand we need to give to those robots the ability to discriminate between reliable and unreliable informants. This thesis focused on the second problem, presenting an interdisciplinary investigation of trust, in particular a computational model based on neuroscientific and psychological assumptions. First of all, the use of Bayesian networks for modelling causal relationships was investigated. This approach follows the well known theory-theory framework of the Theory of Mind (ToM) and an established line of research based on the Bayesian description of mental processes. Next, the role of gaze in human-robot interaction has been investigated. The results of this research were used to design a head pose estimation system based on Convolutional Neural Networks. The system can be used in robotic platforms to facilitate joint attention tasks and enhance trust. Finally, everything was integrated into a structured cognitive architecture. The architecture is based on an actor-critic reinforcement learning framework and an intrinsic motivation feedback given by a Bayesian network. In order to evaluate the model, the architecture was embodied in the iCub humanoid robot and used to replicate a developmental experiment. The model provides a plausible description of children's reasoning that sheds some light on the underlying mechanism involved in trust-based learning. In the last part of the thesis the contribution of human-robot interaction research is discussed, with the aim of understanding the factors that influence the establishment of trust during joint tasks. Overall, this thesis provides a computational model of trust that takes into account the development of cognitive abilities in children, with a particular emphasis on the ToM and the underlying neural dynamics.THRIVE, Air Force Office of Scientific Research, Award No. FA9550-15-1-002