19 research outputs found
Rain Removal in Traffic Surveillance: Does it Matter?
Varying weather conditions, including rainfall and snowfall, are generally
regarded as a challenge for computer vision algorithms. One proposed solution
to the challenges induced by rain and snowfall is to artificially remove the
rain from images or video using rain removal algorithms. It is the promise of
these algorithms that the rain-removed image frames will improve the
performance of subsequent segmentation and tracking algorithms. However, rain
removal algorithms are typically evaluated on their ability to remove synthetic
rain on a small subset of images. Currently, their behavior is unknown on
real-world videos when integrated with a typical computer vision pipeline. In
this paper, we review the existing rain removal algorithms and propose a new
dataset that consists of 22 traffic surveillance sequences under a broad
variety of weather conditions that all include either rain or snowfall. We
propose a new evaluation protocol that evaluates the rain removal algorithms on
their ability to improve the performance of subsequent segmentation, instance
segmentation, and feature tracking algorithms under rain and snow. If
successful, the de-rained frames of a rain removal algorithm should improve
segmentation performance and increase the number of accurately tracked
features. The results show that a recent single-frame-based rain removal
algorithm increases the segmentation performance by 19.7% on our proposed
dataset, but it eventually decreases the feature tracking performance and
showed mixed results with recent instance segmentation methods. However, the
best video-based rain removal algorithm improves the feature tracking accuracy
by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System
Enhanced DCP filter for Real-World Hazy Scenes
Haze is an atmospheric phenomenon that considerably degrades the visibility of out- door scenes. This happens due to atmosphere particles that absorb and disperse the sunshine. This paper introduces a unique single image visibility restoration algorithm that enhances visibility of such corrupted pictures. A unique edge-preserving decomposition-based technique is prepared to estimate transmission map for a haze image. Therefore, haze removal algorithmic rule has been taken from Koschmiedars law that includes a quick replacement-variation approach to dehaze and denoise at the same time. The proposed technique Enhanced DCP Filter (EDCPF) initially estimates a transmission map employing a windows adaptive technique that supported the dark channel. Restoration of foggy images is an important issue for the de-weathering in computer vision. A new method has been introduced for estimating the optical transmission in hazy scenes. Based on this estimation, the scattered light is eliminated to increase scene visibility and recover haze-free scenes
Visibility Video Detection with Dark Channel Prior on Highway
Dark channel prior (DCP) has advantages in image enhancement and image haze removal and is explored to detect highway visibility according to the physical relationship between transmittance and extinction coefficient. However, there are three major error sources in calculating transmittance. The first is that sky regions do not satisfy the assumptions of DCP algorithm. So the optimization algorithms combined with region growing and coefficient correction method are proposed. When extracting atmospheric brightness, different values lead to the second error. Therefore, according to different visibility conditions, a multimode classification method is designed. Image blocky effect causes the third error. Then guided image filtering is introduced to obtain accurate transmittance of each pixel of image. Next, according to the definition meteorological optical visual range and the relationship between transmittance and extinction coefficient of Lambert-Beer’s Law, accurate visibility value can be calculated. A comparative experimental system including visibility detector and video camera was set up to verify the accuracy of these optimization algorithms. Finally, a large number of highway section videos were selected to test the validity of DCP method in different models. The results indicate that these detection visibility methods are feasible and reliable for the smooth operation of highways
Signals and Images in Sea Technologies
Life below water is the 14th Sustainable Development Goal (SDG) envisaged by the United Nations and is aimed at conserving and sustainably using the oceans, seas, and marine resources for sustainable development. It is not difficult to argue that signals and image technologies may play an essential role in achieving the foreseen targets linked to SDG 14. Besides increasing the general knowledge of ocean health by means of data analysis, methodologies based on signal and image processing can be helpful in environmental monitoring, in protecting and restoring ecosystems, in finding new sensor technologies for green routing and eco-friendly ships, in providing tools for implementing best practices for sustainable fishing, as well as in defining frameworks and intelligent systems for enforcing sea law and making the sea a safer and more secure place. Imaging is also a key element for the exploration of the underwater world for various scopes, ranging from the predictive maintenance of sub-sea pipelines and other infrastructure projects, to the discovery, documentation, and protection of sunken cultural heritage. The scope of this Special Issue encompasses investigations into techniques and ICT approaches and, in particular, the study and application of signal- and image-based methods and, in turn, exploration of the advantages of their application in the previously mentioned areas
Aplicações de IoT no contexto de uma cidade inteligente
Over the last few years, Smart City solutions mature very rapidly alongside IoT
and cloud computing. These technologies made it easier to create services and
incorporate applications devoted to improving citizen’s quality of life and offer
ways for businesses to implement their solutions. Through rapid advances in the
quality of sensors, new methods emerged, combining different types of devices to
create a better picture of the environment. The purpose of this dissertation is to
provide useful information thought public services, that can be accessed by people
visiting or residing in the beach area of Costa Nova and Barra. It also provides
a solution for the traffic classification problem that projects based on radar data
tend to face. These applications take advantage of the devices implemented in the
PASMO project, such as parking sensors, radars, and CCTV cameras. By making
the service public, businesses have the opportunity to build applications on top of it,
utilizing the sensor data without being directly connected to the data storage. The
example developed in this dissertation offers a dashboard experience where users
can navigate through charts that provide a variety of data and real-time maps. It
also provides a public API that researchers and businesses can use to develop new
applications in the context of PASMO. The other area tackled in this document is
traffic classification. Although the data provided is reliable for the most part, one
big issue is the accuracy of vehicle classification provided by the radar. Still, this
device offers precise values when it comes to detection, with the cameras doing a
good job in classifying traffic. The goal is to combine these two devices to present
much precise information, using state-of-the-art object detection algorithms and
sensor fusion methods. In the end, the system will enrich the PASMO project by
making its data easily available to the public while correcting the accuracy problems
of some devices.Nos últimos anos, as soluções Smart City amadurecem muito rapidamente em conjunto
com IoT e serviços na cloud. Estas tecnologias facilitam a criação de serviços
e a incorporação de aplicações direcionados á melhoria da qualidade de vida do cidadão,
oferecendo formas das empresas implementarem suas soluções. Por meio de
rápidos avanços na qualidade dos sensores, novos métodos surgiram, combinando
diferentes tipos de dispositivos para criar uma melhor imagem da realidade. O
objetivo desta dissertação é fornecer informações úteis através de serviços públicos,
que podem ser acedidos por pessoas que visitam ou residem na Costa Nova e
Barra. Também fornece uma solução para o problema de classificação de tráfego
que projetos baseados em dados de radar tendem a enfrentar. Estas aplicações
beneficiam dos dispositivos implementados no projeto PASMO, como sensores de
estacionamento, radares e câmeras de CFTV. Ao disponibilizar os serviços publicamente,
as empresas têm a oportunidade de construir as suas próprias aplicações
em cima destes, usando os dados dos sensores sem estar diretamente conectado
ao armazenamento de dados. O exemplo desenvolvido nesta dissertação oferece
uma experiência de dashboard onde os utilizadores podem navegar por gráficos que
fornecem uma variedade de dados e mapas em tempo real. Também fornece uma
API pública que os investigadores e empresas podem usar para desenvolver novos
aplicativos no contexto do PASMO. A outra área abordada neste documento é a
classificação de tráfego. Embora os dados fornecidos sejam confiáveis, um grande
problema provém da precisão da classificação dos veículos fornecida pelo radar.
Ainda assim, este dispositivo oferece valores precisos quando se trata de detecção,
com as câmeras fazendo um bom trabalho na parte de classificação do tráfego. O
objetivo é combinar estes dois dispositivos para apresentar informações corretas,
usando algoritmos de detecção de objetos e métodos de fusão de sensores. No
final, o sistema irá enriquecer o projeto PASMO, tornando seus dados facilmente
disponíveis ao público e corrigindo problemas de precisão de alguns dispositivos.Mestrado em Engenharia de Computadores e Telemátic