5,297 research outputs found
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
Crowd Disasters as Systemic Failures: Analysis of the Love Parade Disaster
Each year, crowd disasters happen in different areas of the world. How and
why do such disasters happen? Are the fatalities caused by relentless behavior
of people or a psychological state of panic that makes the crowd 'go mad'? Or
are they a tragic consequence of a breakdown of coordination? These and other
questions are addressed, based on a qualitative analysis of publicly available
videos and materials, which document the planning and organization of the Love
Parade in Duisburg, Germany, and the crowd disaster on July 24, 2010. Our
analysis reveals a number of misunderstandings that have widely spread. We also
provide a new perspective on concepts such as 'intentional pushing', 'mass
panic', 'stampede', and 'crowd crushs'. The focus of our analysis is on the
contributing causal factors and their mutual interdependencies, not on legal
issues or the judgment of personal or institutional responsibilities. Video
recordings show that, in Duisburg, people stumbled and piled up due to a
'domino effect', resulting from a phenomenon called 'crowd turbulence' or
'crowd quake'. Crowd quakes are a typical reason for crowd disasters, to be
distinguished from crowd disasters resulting from 'panic stampedes' or 'crowd
crushes'. In Duisburg, crowd turbulence was the consequence of amplifying
feedback and cascading effects, which are typical for systemic instabilities.
Accordingly, things can go terribly wrong in spite of no bad intentions from
anyone. Comparing the incident in Duisburg with others, we give recommendations
to help prevent future crowd disasters. In particular, we introduce a new scale
to assess the criticality of conditions in the crowd. This may allow
preventative measures to be taken earlier on. Furthermore, we discuss the
merits and limitations of citizen science for public investigation, considering
that today, almost every event is recorded and reflected in the World Wide Web.Comment: For a collection of links to complementary video materials see
http://loveparadevideos.heroku.com/ For related work see
http://www.soms.ethz.c
Application-aware optimization of Artificial Intelligence for deployment on resource constrained devices
Artificial intelligence (AI) is changing people's everyday life. AI techniques such as Deep Neural Networks (DNN) rely on heavy computational models, which are in principle designed to be executed on powerful HW platforms, such as desktop or server environments. However, the increasing need to apply such solutions in people's everyday life has encouraged the research for methods to allow their deployment on embedded, portable and stand-alone devices, such as mobile phones, which exhibit relatively low memory and computational resources. Such methods targets both the development of lightweight AI algorithms and their acceleration through dedicated HW.
This thesis focuses on the development of lightweight AI solutions, with attention to deep neural networks, to facilitate their deployment on resource constrained devices. Focusing on the computer vision field, we show how putting together the self learning ability of deep neural networks with application-specific knowledge, in the form of feature engineering, it is possible to dramatically reduce the total memory and computational burden, thus allowing the deployment on edge devices. The proposed approach aims to be complementary to already existing application-independent network compression solutions. In this work three main DNN optimization goals have been considered: increasing speed and accuracy, allowing training at the edge, and allowing execution on a microcontroller. For each of these we deployed the resulting algorithm to the target embedded device and measured its performance
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