3,618 research outputs found
Panoramic optical and near-infrared SETI instrument: optical and structural design concepts
We propose a novel instrument design to greatly expand the current optical
and near-infrared SETI search parameter space by monitoring the entire
observable sky during all observable time. This instrument is aimed to search
for technosignatures by means of detecting nano- to micro-second light pulses
that could have been emitted, for instance, for the purpose of interstellar
communications or energy transfer. We present an instrument conceptual design
based upon an assembly of 198 refracting 0.5-m telescopes tessellating two
geodesic domes. This design produces a regular layout of hexagonal collecting
apertures that optimizes the instrument footprint, aperture diameter,
instrument sensitivity and total field-of-view coverage. We also present the
optical performance of some Fresnel lenses envisaged to develop a dedicated
panoramic SETI (PANOSETI) observatory that will dramatically increase sky-area
searched (pi steradians per dome), wavelength range covered, number of stellar
systems observed, interstellar space examined and duration of time monitored
with respect to previous optical and near-infrared technosignature finders.Comment: 14 pages, 5 figures, 3 table
Multi-modal video analysis for early fire detection
In dit proefschrift worden verschillende aspecten van een intelligent videogebaseerd branddetectiesysteem onderzocht. In een eerste luik ligt de nadruk op de multimodale verwerking van visuele, infrarood en time-of-flight videobeelden, die de louter visuele detectie verbetert. Om de verwerkingskost zo minimaal mogelijk te houden, met het oog op real-time detectie, is er voor elk van het type sensoren een set ’low-cost’ brandkarakteristieken geselecteerd die vuur en vlammen uniek beschrijven. Door het samenvoegen van de verschillende typen informatie kunnen het aantal gemiste detecties en valse alarmen worden gereduceerd, wat resulteert in een significante verbetering van videogebaseerde branddetectie. Om de multimodale detectieresultaten te kunnen combineren, dienen de multimodale beelden wel geregistreerd (~gealigneerd) te zijn. Het tweede luik van dit proefschrift focust zich hoofdzakelijk op dit samenvoegen van multimodale data en behandelt een nieuwe silhouet gebaseerde registratiemethode. In het derde en tevens laatste luik van dit proefschrift worden methodes voorgesteld om videogebaseerde brandanalyse, en in een latere fase ook brandmodellering, uit te voeren. Elk van de voorgestelde technieken voor multimodale detectie en multi-view lokalisatie zijn uitvoerig getest in de praktijk. Zo werden onder andere succesvolle testen uitgevoerd voor de vroegtijdige detectie van wagenbranden in ondergrondse parkeergarages
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
Indoor Smoking Detection Based on YOLO Framework with Infrared Image
This study recommends combining the efficacy of YOLO with the greater visibility provided by infrared imaging to create a better indoor smoking detection system. The YOLO system divides photos into a grid and anticipates bounding boxes and class probabilities at the same time, making it an obvious choice for its real-time item detection capabilities. The approach improves its robustness by identifying heat signals associated with smoking sessions and overcoming limitations posed by low-light or blocked circumstances. The addition of infrared images significantly improved the system's performance in low-light conditions. A dual spectrum thermal camera is used in the entire indoor smoking detection system to obtain a large collection of infrared images representing various interior locations with documented smoking episodes. During the training phase, data augmentation processes such as random rotations, flips, and brightness and contrast fluctuations were used to improve the system's performance. The CIoU loss function improved the system's localization accuracy significantly, reducing false positives and improving overall detection performance. The combination of YOLO and infrared photography, in conjunction with data augmentation and the CIoU loss function, not only improves indoor smoking detection but also demonstrates the benefits of merging several technologies in the development of more effective and adaptive systems
Vehicle classification in intelligent transport systems: an overview, methods and software perspective
Vehicle Classification (VC) is a key element of Intelligent Transportation Systems (ITS). Diverse ranges of ITS applications like security systems, surveillance frameworks, fleet monitoring, traffic safety, and automated parking are using VC. Basically, in the current VC methods, vehicles are classified locally as a vehicle passes through a monitoring area, by fixed sensors or using a compound method. This paper presents a pervasive study on the state of the art of VC methods. We introduce a detailed VC taxonomy and explore the different kinds of traffic information that can be extracted via each method. Subsequently, traditional and cutting edge VC systems are investigated from different aspects. Specifically, strengths and shortcomings of the existing VC methods are discussed and real-time alternatives like Vehicular Ad-hoc Networks (VANETs) are investigated to convey physical as well as kinematic characteristics of the vehicles. Finally, we review a broad range of soft computing solutions involved in VC in the context of machine learning, neural networks, miscellaneous features, models and other methods
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