Action recognition based on efficient deep feature learning in the spatio-temporal domain

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2D convolutional neural network extended to a concatenated 3D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for content-based recognition of videos. Relying on a 2D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available.Peer ReviewedPostprint (author's final draft

Pedestrian Models for Robot Motion

We discuss the development of a robot system able to replicate human group motion and show how a pedestrian model may be converted to a robot control system in order to achieve this goal

Robot-on-Robot Gossiping to Improve Sense of Human-Robot Conversation

S. Mitsuno, Y. Yoshikawa and H. Ishiguro, "Robot-on-Robot Gossiping to Improve Sense of Human-Robot Conversation," 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Naples, Italy, 2020, pp. 653-658, doi: 10.1109/RO-MAN47096.2020.9223442.The 29th IEEE International Conference on Robot & Human Interactive Communication [31 AUG - 04 SEPT, 2020

Predictive Modeling of Pedestrian Motion Patterns with Bayesian Nonparametrics

For safe navigation in dynamic environments, an autonomous vehicle must be able to identify and predict the future behaviors of other mobile agents. A promising data-driven approach is to learn motion patterns from previous observations using Gaussian process (GP) regression, which are then used for online prediction. GP mixture models have been subsequently proposed for finding the number of motion patterns using GP likelihood as a similarity metric. However, this paper shows that using GP likelihood as a similarity metric can lead to non-intuitive clustering configurations - such as grouping trajectories with a small planar shift with respect to each other into different clusters - and thus produce poor prediction results. In this paper we develop a novel modeling framework, Dirichlet process active region (DPAR), that addresses the deficiencies of the previous GP-based approaches. In particular, with a discretized representation of the environment, we can explicitly account for planar shifts via a max pooling step, and reduce the computational complexity of the statistical inference procedure compared with the GP-based approaches. The proposed algorithm was applied on two real pedestrian trajectory datasets collected using a 3D Velodyne Lidar, and showed 15% improvement in prediction accuracy and 4.2 times reduction in computational time compared with a GP-based algorithm.Ford Motor Compan

Challenges with Voice Assistants for the Elderly in Semi-Public Spaces

Voice Assistants such as Amazon Alexa and Google Home have recently made inroads into all walks of life as is evident from their popularity, and the growing number of users. Traditionally, research on elderly people with voice assistants has focused on private spaces - bedrooms, kitchens, and living rooms. Due to privacy concerns, ethical issues, legal issues, and noisy environments, their use in public and semi-public spaces are discouraged. However, by carefully mitigating these concerns, voice assistants could still find applications in semi-public spaces for elderly people. This paper summarizes the preliminary insights from 8 interviews that we conducted with elderly people and throws light on the potential areas where voice assistants could be used in semi-public spaces.Peer reviewe

Including Front-Line Workers as Primary Stakeholders in Public-Space HRI

When a robot is deployed in a public space, that space is almost always an existing workspace, with front-line workers who will need to work alongside the robot when it is deployed and who are crucial to the success of the overall project. We show how these front-line workers have been included alongside other stakeholders in three recent social robotics projects: a socially assistive robot for use in paediatric emergency departments, a guidance robot for visitors to a large university building, and a robot social worker designed to help international students and other new arrivals to navigate processes in a new country. We argue that the contributions of these front-line workers are crucial to the success of any such public-space and should always be taken into account at all stages of the project life cycle