12,068 research outputs found
InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure
Volumetric models have become a popular representation for 3D scenes in
recent years. One breakthrough leading to their popularity was KinectFusion,
which focuses on 3D reconstruction using RGB-D sensors. However, monocular SLAM
has since also been tackled with very similar approaches. Representing the
reconstruction volumetrically as a TSDF leads to most of the simplicity and
efficiency that can be achieved with GPU implementations of these systems.
However, this representation is memory-intensive and limits applicability to
small-scale reconstructions. Several avenues have been explored to overcome
this. With the aim of summarizing them and providing for a fast, flexible 3D
reconstruction pipeline, we propose a new, unifying framework called InfiniTAM.
The idea is that steps like camera tracking, scene representation and
integration of new data can easily be replaced and adapted to the user's needs.
This report describes the technical implementation details of InfiniTAM v3,
the third version of our InfiniTAM system. We have added various new features,
as well as making numerous enhancements to the low-level code that
significantly improve our camera tracking performance. The new features that we
expect to be of most interest are (i) a robust camera tracking module; (ii) an
implementation of Glocker et al.'s keyframe-based random ferns camera
relocaliser; (iii) a novel approach to globally-consistent TSDF-based
reconstruction, based on dividing the scene into rigid submaps and optimising
the relative poses between them; and (iv) an implementation of Keller et al.'s
surfel-based reconstruction approach.Comment: This article largely supersedes arxiv:1410.0925 (it describes version
3 of the InfiniTAM framework
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
Segmentation-based video coding system allowing the manipulation of objects
This paper presents a generic video coding algorithm allowing the content-based manipulation of objects. This manipulation is possible thanks to the definition of a spatiotemporal segmentation of the sequences. The coding strategy relies on a joint optimization in the rate-distortion sense of the partition definition and of the coding techniques to be used within each region. This optimization creates the link between the analysis and synthesis parts of the coder. The analysis defines the time evolution of the partition, as well as the elimination or the appearance of regions that are homogeneous either spatially or in motion. The coding of the texture as well as of the partition relies on region-based motion compensation techniques. The algorithm offers a good compromise between the ability to track and manipulate objects and the coding efficiency.Peer ReviewedPostprint (published version
Video surveillance systems-current status and future trends
Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance
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