24,968 research outputs found
Ubiquitous Human Perception for Real-Time Gender Estimation
Abstract -In environments where robotic systems are deployed people often have different requirements for the robotic services and human-robot interaction methods. This paper presents a robotic system that exploits the advantages of ubiquitous perception in order to gather knowledge from multiple sensors and various modalities. This ubiquitous human perception will facilitate user profiling in order to support personalised services and individual human-robot interaction. This system combines ubiquitous smart sensing, methods of multi-modal human perception and existing human recognition algorithms from the field of biometrics to collectively work towards a real-time, robust and scalable solution for gender estimation
Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective
Blind people have limited access to information about their surroundings,
which is important for ensuring one's safety, managing social interactions, and
identifying approaching pedestrians. With advances in computer vision, wearable
cameras can provide equitable access to such information. However, the
always-on nature of these assistive technologies poses privacy concerns for
parties that may get recorded. We explore this tension from both perspectives,
those of sighted passersby and blind users, taking into account camera
visibility, in-person versus remote experience, and extracted visual
information. We conduct two studies: an online survey with MTurkers (N=206) and
an in-person experience study between pairs of blind (N=10) and sighted (N=40)
participants, where blind participants wear a working prototype for pedestrian
detection and pass by sighted participants. Our results suggest that both of
the perspectives of users and bystanders and the several factors mentioned
above need to be carefully considered to mitigate potential social tensions.Comment: The 2020 ACM CHI Conference on Human Factors in Computing Systems
(CHI 2020
Cultural Diffusion and Trends in Facebook Photographs
Online social media is a social vehicle in which people share various moments
of their lives with their friends, such as playing sports, cooking dinner or
just taking a selfie for fun, via visual means, that is, photographs. Our study
takes a closer look at the popular visual concepts illustrating various
cultural lifestyles from aggregated, de-identified photographs. We perform
analysis both at macroscopic and microscopic levels, to gain novel insights
about global and local visual trends as well as the dynamics of interpersonal
cultural exchange and diffusion among Facebook friends. We processed images by
automatically classifying the visual content by a convolutional neural network
(CNN). Through various statistical tests, we find that socially tied
individuals more likely post images showing similar cultural lifestyles. To
further identify the main cause of the observed social correlation, we use the
Shuffle test and the Preference-based Matched Estimation (PME) test to
distinguish the effects of influence and homophily. The results indicate that
the visual content of each user's photographs are temporally, although not
necessarily causally, correlated with the photographs of their friends, which
may suggest the effect of influence. Our paper demonstrates that Facebook
photographs exhibit diverse cultural lifestyles and preferences and that the
social interaction mediated through the visual channel in social media can be
an effective mechanism for cultural diffusion.Comment: 10 pages, To appear in ICWSM 2017 (Full Paper
Effects of White Space on Consumer Perceptions of Value in E-Commerce
As e-commerce becomes an increasingly large industry, questions remain about how the isolated effects of design elements on websites influence consumer perceptions and purchasing behavior. This study used a quantitative approach to measuring the effect of a ubiquitous element of design, white space, on the perception of the monetary value of individual items. White space is a key component of design and website usability, yet it has been shown to be related to the perception of luxury. Little is known about the direct relationship between manipulation of white space and the outcomes on consumer perceptions of value in an e-commerce context. This study found no significant difference between two levels of total white space area (large vs. small) measured by participants\u27 perceived cost of items (chairs). In contrast, while holding total white space constant, the effect of white space distance between images was significant for males but not for females. Additionally, no significant relationship between gender and frequency of online shopping behavior was found, χ2(1) = 3.19, p = .07, ϕ = .17. Gender and amount of time spent per month online were significantly related, χ2(1) = 6.21, p = .013, ϕ = .24
AutoTherm: A Dataset and Ablation Study for Thermal Comfort Prediction in Vehicles
State recognition in well-known and customizable environments such as
vehicles enables novel insights into users and potentially their intentions.
Besides safety-relevant insights into, for example, fatigue, user
experience-related assessments become increasingly relevant. As thermal comfort
is vital for overall comfort, we introduce a dataset for its prediction in
vehicles incorporating 31 input signals and self-labeled user ratings based on
a 7-point Likert scale (-3 to +3) by 21 subjects. An importance ranking of such
signals indicates higher impact on prediction for signals like ambient
temperature, ambient humidity, radiation temperature, and skin temperature.
Leveraging modern machine learning architectures enables us to not only
automatically recognize human thermal comfort state but also predict future
states. We provide details on how we train a recurrent network-based classifier
and, thus, perform an initial performance benchmark of our proposed thermal
comfort dataset. Ultimately, we compare our collected dataset to publicly
available datasets
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