71,049 research outputs found
First impressions: A survey on vision-based apparent personality trait analysis
© 2019 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.Personality analysis has been widely studied in psychology, neuropsychology, and signal processing fields, among others. From the past few years, it also became an attractive research area in visual computing. From the computational point of view, by far speech and text have been the most considered cues of information for analyzing personality. However, recently there has been an increasing interest from the computer vision community in analyzing personality from visual data. Recent computer vision approaches are able to accurately analyze human faces, body postures and behaviors, and use these information to infer apparent personality traits. Because of the overwhelming research interest in this topic, and of the potential impact that this sort of methods could have in society, we present in this paper an up-to-date review of existing vision-based approaches for apparent personality trait recognition. We describe seminal and cutting edge works on the subject, discussing and comparing their distinctive features and limitations. Future venues of research in the field are identified and discussed. Furthermore, aspects on the subjectivity in data labeling/evaluation, as well as current datasets and challenges organized to push the research on the field are reviewed.Peer ReviewedPostprint (author's final draft
Multichannel Attention Network for Analyzing Visual Behavior in Public Speaking
Public speaking is an important aspect of human communication and
interaction. The majority of computational work on public speaking concentrates
on analyzing the spoken content, and the verbal behavior of the speakers. While
the success of public speaking largely depends on the content of the talk, and
the verbal behavior, non-verbal (visual) cues, such as gestures and physical
appearance also play a significant role. This paper investigates the importance
of visual cues by estimating their contribution towards predicting the
popularity of a public lecture. For this purpose, we constructed a large
database of more than TED talk videos. As a measure of popularity of the
TED talks, we leverage the corresponding (online) viewers' ratings from
YouTube. Visual cues related to facial and physical appearance, facial
expressions, and pose variations are extracted from the video frames using
convolutional neural network (CNN) models. Thereafter, an attention-based long
short-term memory (LSTM) network is proposed to predict the video popularity
from the sequence of visual features. The proposed network achieves
state-of-the-art prediction accuracy indicating that visual cues alone contain
highly predictive information about the popularity of a talk. Furthermore, our
network learns a human-like attention mechanism, which is particularly useful
for interpretability, i.e. how attention varies with time, and across different
visual cues by indicating their relative importance
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
- …