7,060 research outputs found
SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
Studying free-standing conversational groups (FCGs) in unstructured social
settings (e.g., cocktail party ) is gratifying due to the wealth of information
available at the group (mining social networks) and individual (recognizing
native behavioral and personality traits) levels. However, analyzing social
scenes involving FCGs is also highly challenging due to the difficulty in
extracting behavioral cues such as target locations, their speaking activity
and head/body pose due to crowdedness and presence of extreme occlusions. To
this end, we propose SALSA, a novel dataset facilitating multimodal and
Synergetic sociAL Scene Analysis, and make two main contributions to research
on automated social interaction analysis: (1) SALSA records social interactions
among 18 participants in a natural, indoor environment for over 60 minutes,
under the poster presentation and cocktail party contexts presenting
difficulties in the form of low-resolution images, lighting variations,
numerous occlusions, reverberations and interfering sound sources; (2) To
alleviate these problems we facilitate multimodal analysis by recording the
social interplay using four static surveillance cameras and sociometric badges
worn by each participant, comprising the microphone, accelerometer, bluetooth
and infrared sensors. In addition to raw data, we also provide annotations
concerning individuals' personality as well as their position, head, body
orientation and F-formation information over the entire event duration. Through
extensive experiments with state-of-the-art approaches, we show (a) the
limitations of current methods and (b) how the recorded multiple cues
synergetically aid automatic analysis of social interactions. SALSA is
available at http://tev.fbk.eu/salsa.Comment: 14 pages, 11 figure
An Indoor Navigation System Using a Sensor Fusion Scheme on Android Platform
With the development of wireless communication networks, smart phones have become a necessity for people’s daily lives, and they meet not only the needs of basic functions for users such as sending a message or making a phone call, but also the users’ demands for entertainment, surfing the Internet and socializing. Navigation functions have been commonly utilized, however the navigation function is often based on GPS (Global Positioning System) in outdoor environments, whereas a number of applications need to navigate indoors. This paper presents a system to achieve high accurate indoor navigation based on Android platform. To do this, we design a sensor fusion scheme for our system. We divide the system into three main modules: distance measurement module, orientation detection module and position update module. We use an efficient way to estimate the stride length and use step sensor to count steps in distance measurement module. For orientation detection module, in order to get the optimal result of orientation, we then introduce Kalman filter to de-noise the data collected from different sensors. In the last module, we combine the data from the previous modules and calculate the current location. Results of experiments show that our system works well and has high accuracy in indoor situations
An Intra-Subject Approach Based on the Application of HMM to Predict Concentration in Educational Contexts from Nonintrusive Physiological Signals in Real-World Situations
Previous research has proven the strong influence of emotions on student engagement and
motivation. Therefore, emotion recognition is becoming very relevant in educational scenarios, but
there is no standard method for predicting students’ affects. However, physiological signals have
been widely used in educational contexts. Some physiological signals have shown a high accuracy
in detecting emotions because they reflect spontaneous affect-related information, which is fresh
and does not require additional control or interpretation. Most proposed works use measuring
equipment for which applicability in real-world scenarios is limited because of its high cost and
intrusiveness. To tackle this problem, in this work, we analyse the feasibility of developing low-cost
and nonintrusive devices to obtain a high detection accuracy from easy-to-capture signals. By using
both inter-subject and intra-subject models, we present an experimental study that aims to explore
the potential application of Hidden Markov Models (HMM) to predict the concentration state from
4 commonly used physiological signals, namely heart rate, breath rate, skin conductance and skin
temperature. We also study the effect of combining these four signals and analyse their potential use
in an educational context in terms of intrusiveness, cost and accuracy. The results show that a high
accuracy can be achieved with three of the signals when using HMM-based intra-subject models.
However, inter-subject models, which are meant to obtain subject-independent approaches for affect
detection, fail at the same task.This research was partly supported by Spanish Ministry of Science, Innovation and Universities through projects PGC2018-096463-B-I00 and PGC2018-102279-B-I00 (MCIU/AEI/FEDER, UE)
Resilient networking in wireless sensor networks
This report deals with security in wireless sensor networks (WSNs),
especially in network layer. Multiple secure routing protocols have been
proposed in the literature. However, they often use the cryptography to secure
routing functionalities. The cryptography alone is not enough to defend against
multiple attacks due to the node compromise. Therefore, we need more
algorithmic solutions. In this report, we focus on the behavior of routing
protocols to determine which properties make them more resilient to attacks.
Our aim is to find some answers to the following questions. Are there any
existing protocols, not designed initially for security, but which already
contain some inherently resilient properties against attacks under which some
portion of the network nodes is compromised? If yes, which specific behaviors
are making these protocols more resilient? We propose in this report an
overview of security strategies for WSNs in general, including existing attacks
and defensive measures. In this report we focus at the network layer in
particular, and an analysis of the behavior of four particular routing
protocols is provided to determine their inherent resiliency to insider
attacks. The protocols considered are: Dynamic Source Routing (DSR),
Gradient-Based Routing (GBR), Greedy Forwarding (GF) and Random Walk Routing
(RWR)
ConfLab: A Rich Multimodal Multisensor Dataset of Free-Standing Social Interactions in the Wild
Recording the dynamics of unscripted human interactions in the wild is
challenging due to the delicate trade-offs between several factors: participant
privacy, ecological validity, data fidelity, and logistical overheads. To
address these, following a 'datasets for the community by the community' ethos,
we propose the Conference Living Lab (ConfLab): a new concept for multimodal
multisensor data collection of in-the-wild free-standing social conversations.
For the first instantiation of ConfLab described here, we organized a real-life
professional networking event at a major international conference. Involving 48
conference attendees, the dataset captures a diverse mix of status,
acquaintance, and networking motivations. Our capture setup improves upon the
data fidelity of prior in-the-wild datasets while retaining privacy
sensitivity: 8 videos (1920x1080, 60 fps) from a non-invasive overhead view,
and custom wearable sensors with onboard recording of body motion (full 9-axis
IMU), privacy-preserving low-frequency audio (1250 Hz), and Bluetooth-based
proximity. Additionally, we developed custom solutions for distributed hardware
synchronization at acquisition, and time-efficient continuous annotation of
body keypoints and actions at high sampling rates. Our benchmarks showcase some
of the open research tasks related to in-the-wild privacy-preserving social
data analysis: keypoints detection from overhead camera views, skeleton-based
no-audio speaker detection, and F-formation detection.Comment: v2 is the version submitted to Neurips 2022 Datasets and Benchmarks
Trac
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
An Effective Approach to Nonparametric Quickest Detection and Its Decentralized Realization
This dissertation focuses on the study of nonparametric quickest detection and its decentralized implementation in a distributed environment. Quickest detection schemes are geared toward detecting a change in the state of a data stream or a real-time process. Classical quickest detection schemes invariably assume knowledge of the pre-change and post-change distributions that may not be available in many applications. A distribution free nonparametric quickest detection procedure is presented based on a novel distance measure, referred to as the Q-Q distance calculated from the Quantile-Quantile plot. Theoretical analysis of the distance measure and detection procedure is presented to justify the proposed algorithm and provide performance guarantees. The Q-Q distance based detection procedure presents comparable performance compared to classical parametric detection procedure and better performance than other nonparametric procedures. The proposed procedure is most effective when detecting small changes. As the technology advances, distributed sensing and detection become feasible. Existing decentralized detection approaches are largely parametric. The decentralized realization of Q-Q distance based nonparametric quickest detection scheme is further studied, where data streams are simultaneously collected from multiple channels located distributively to jointly reach a detection decision. Two implementation schemes, binary quickest detection and local decision fusion, are described. Experimental results show that the proposed method has a comparable performance to the benchmark parametric cumulative sum (CUSUM) test in binary detection. Finally the dissertation concludes with a summary of the contributions to the state of the art
- …