16,002 research outputs found
The path inference filter: model-based low-latency map matching of probe vehicle data
We consider the problem of reconstructing vehicle trajectories from sparse
sequences of GPS points, for which the sampling interval is between 10 seconds
and 2 minutes. We introduce a new class of algorithms, called altogether path
inference filter (PIF), that maps GPS data in real time, for a variety of
trade-offs and scenarios, and with a high throughput. Numerous prior approaches
in map-matching can be shown to be special cases of the path inference filter
presented in this article. We present an efficient procedure for automatically
training the filter on new data, with or without ground truth observations. The
framework is evaluated on a large San Francisco taxi dataset and is shown to
improve upon the current state of the art. This filter also provides insights
about driving patterns of drivers. The path inference filter has been deployed
at an industrial scale inside the Mobile Millennium traffic information system,
and is used to map fleets of data in San Francisco, Sacramento, Stockholm and
Porto.Comment: Preprint, 23 pages and 23 figure
ProSLAM: Graph SLAM from a Programmer's Perspective
In this paper we present ProSLAM, a lightweight stereo visual SLAM system
designed with simplicity in mind. Our work stems from the experience gathered
by the authors while teaching SLAM to students and aims at providing a highly
modular system that can be easily implemented and understood. Rather than
focusing on the well known mathematical aspects of Stereo Visual SLAM, in this
work we highlight the data structures and the algorithmic aspects that one
needs to tackle during the design of such a system. We implemented ProSLAM
using the C++ programming language in combination with a minimal set of well
known used external libraries. In addition to an open source implementation, we
provide several code snippets that address the core aspects of our approach
directly in this paper. The results of a thorough validation performed on
standard benchmark datasets show that our approach achieves accuracy comparable
to state of the art methods, while requiring substantially less computational
resources.Comment: 8 pages, 8 figure
CiNCT: Compression and retrieval for massive vehicular trajectories via relative movement labeling
In this paper, we present a compressed data structure for moving object
trajectories in a road network, which are represented as sequences of road
edges. Unlike existing compression methods for trajectories in a network, our
method supports pattern matching and decompression from an arbitrary position
while retaining a high compressibility with theoretical guarantees.
Specifically, our method is based on FM-index, a fast and compact data
structure for pattern matching. To enhance the compression, we incorporate the
sparsity of road networks into the data structure. In particular, we present
the novel concepts of relative movement labeling and PseudoRank, each
contributing to significant reductions in data size and query processing time.
Our theoretical analysis and experimental studies reveal the advantages of our
proposed method as compared to existing trajectory compression methods and
FM-index variants
Visual region understanding: unsupervised extraction and abstraction
The ability to gain a conceptual understanding of the world in uncontrolled environments is the ultimate goal of vision-based computer systems. Technological
societies today are heavily reliant on surveillance and security infrastructure, robotics, medical image analysis, visual data categorisation and search, and smart device user interaction, to name a few. Out of all the complex problems tackled
by computer vision today in context of these technologies, that which lies closest to the original goals of the field is the subarea of unsupervised scene analysis or scene modelling. However, its common use of low level features does not provide
a good balance between generality and discriminative ability, both a result and a symptom of the sensory and semantic gaps existing between low level computer
representations and high level human descriptions.
In this research we explore a general framework that addresses the fundamental
problem of universal unsupervised extraction of semantically meaningful visual
regions and their behaviours. For this purpose we address issues related to
(i) spatial and spatiotemporal segmentation for region extraction, (ii) region shape modelling, and (iii) the online categorisation of visual object classes and the spatiotemporal analysis of their behaviours. Under this framework we propose (a)
a unified region merging method and spatiotemporal region reduction, (b) shape
representation by the optimisation and novel simplication of contour-based growing neural gases, and (c) a foundation for the analysis of visual object motion properties using a shape and appearance based nearest-centroid classification algorithm
and trajectory plots for the obtained region classes.
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Specifically, we formulate a region merging spatial segmentation mechanism
that combines and adapts features shown previously to be individually useful,
namely parallel region growing, the best merge criterion, a time adaptive threshold, and region reduction techniques. For spatiotemporal region refinement we
consider both scalar intensity differences and vector optical flow. To model the shapes of the visual regions thus obtained, we adapt the growing neural gas for
rapid region contour representation and propose a contour simplication technique. A fast unsupervised nearest-centroid online learning technique next groups observed region instances into classes, for which we are then able to analyse spatial
presence and spatiotemporal trajectories. The analysis results show semantic correlations to real world object behaviour. Performance evaluation of all steps across
standard metrics and datasets validate their performance
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