11,627 research outputs found
Radar and RGB-depth sensors for fall detection: a review
This paper reviews recent works in the literature on the use of systems based on radar and RGB-Depth (RGB-D) sensors for fall detection, and discusses outstanding research challenges and trends related to this research field. Systems to detect reliably fall events and promptly alert carers and first responders have gained significant interest in the past few years in order to address the societal issue of an increasing number of elderly people living alone, with the associated risk of them falling and the consequences in terms of health treatments, reduced well-being, and costs. The interest in radar and RGB-D sensors is related to their capability to enable contactless and non-intrusive monitoring, which is an advantage for practical deployment and users’ acceptance and compliance, compared with other sensor technologies, such as video-cameras, or wearables. Furthermore, the possibility of combining and fusing information from The heterogeneous types of sensors is expected to improve the overall performance of practical fall detection systems. Researchers from different fields can benefit from multidisciplinary knowledge and awareness of the latest developments in radar and RGB-D sensors that this paper is discussing
Graph Distillation for Action Detection with Privileged Modalities
We propose a technique that tackles action detection in multimodal videos
under a realistic and challenging condition in which only limited training data
and partially observed modalities are available. Common methods in transfer
learning do not take advantage of the extra modalities potentially available in
the source domain. On the other hand, previous work on multimodal learning only
focuses on a single domain or task and does not handle the modality discrepancy
between training and testing. In this work, we propose a method termed graph
distillation that incorporates rich privileged information from a large-scale
multimodal dataset in the source domain, and improves the learning in the
target domain where training data and modalities are scarce. We evaluate our
approach on action classification and detection tasks in multimodal videos, and
show that our model outperforms the state-of-the-art by a large margin on the
NTU RGB+D and PKU-MMD benchmarks. The code is released at
http://alan.vision/eccv18_graph/.Comment: ECCV 201
Project RISE: Recognizing Industrial Smoke Emissions
Industrial smoke emissions pose a significant concern to human health. Prior
works have shown that using Computer Vision (CV) techniques to identify smoke
as visual evidence can influence the attitude of regulators and empower
citizens to pursue environmental justice. However, existing datasets are not of
sufficient quality nor quantity to train the robust CV models needed to support
air quality advocacy. We introduce RISE, the first large-scale video dataset
for Recognizing Industrial Smoke Emissions. We adopted a citizen science
approach to collaborate with local community members to annotate whether a
video clip has smoke emissions. Our dataset contains 12,567 clips from 19
distinct views from cameras that monitored three industrial facilities. These
daytime clips span 30 days over two years, including all four seasons. We ran
experiments using deep neural networks to establish a strong performance
baseline and reveal smoke recognition challenges. Our survey study discussed
community feedback, and our data analysis displayed opportunities for
integrating citizen scientists and crowd workers into the application of
Artificial Intelligence for social good.Comment: Technical repor
Multi-Modal Trip Hazard Affordance Detection On Construction Sites
Trip hazards are a significant contributor to accidents on construction and
manufacturing sites, where over a third of Australian workplace injuries occur
[1]. Current safety inspections are labour intensive and limited by human
fallibility,making automation of trip hazard detection appealing from both a
safety and economic perspective. Trip hazards present an interesting challenge
to modern learning techniques because they are defined as much by affordance as
by object type; for example wires on a table are not a trip hazard, but can be
if lying on the ground. To address these challenges, we conduct a comprehensive
investigation into the performance characteristics of 11 different colour and
depth fusion approaches, including 4 fusion and one non fusion approach; using
colour and two types of depth images. Trained and tested on over 600 labelled
trip hazards over 4 floors and 2000m in an active construction
site,this approach was able to differentiate between identical objects in
different physical configurations (see Figure 1). Outperforming a colour-only
detector, our multi-modal trip detector fuses colour and depth information to
achieve a 4% absolute improvement in F1-score. These investigative results and
the extensive publicly available dataset moves us one step closer to assistive
or fully automated safety inspection systems on construction sites.Comment: 9 Pages, 12 Figures, 2 Tables, Accepted to Robotics and Automation
Letters (RA-L
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