2,904 research outputs found
Graph Mining for Object Tracking in Videos
International audienceThis paper shows a concrete example of the use of graph mining for tracking objects in videos with moving cameras and without any contextual information on the objects to track. To make the mining algorithm efficient, we benefit from a video representation based on dy- namic (evolving through time) planar graphs. We then define a number of constraints to efficiently find our so-called spatio-temporal graph pat- terns. Those patterns are linked through an occurrences graph to allow us to tackle occlusion or graph features instability problems in the video. Experiments on synthetic and real videos show that our method is effec- tive and allows us to find relevant patterns for our tracking application
Modeling Complex Object Changes in Satellite Image Time-Series: Approach based on CSP and Spatiotemporal Graph
This paper proposes a method for automatically monitoring and analyzing the
evolution of complex geographic objects. The objects are modeled as a
spatiotemporal graph, which separates filiation relations, spatial relations,
and spatiotemporal relations, and is analyzed by detecting frequent sub-graphs
using constraint satisfaction problems (CSP). The process is divided into four
steps: first, the identification of complex objects in each satellite image;
second, the construction of a spatiotemporal graph to model the spatiotemporal
changes of the complex objects; third, the creation of sub-graphs to be
detected in the base spatiotemporal graph; and fourth, the analysis of the
spatiotemporal graph by detecting the sub-graphs and solving a constraint
network to determine relevant sub-graphs. The final step is further broken down
into two sub-steps: (i) the modeling of the constraint network with defined
variables and constraints, and (ii) the solving of the constraint network to
find relevant sub-graphs in the spatiotemporal graph. Experiments were
conducted using real-world satellite images representing several cities in
Saudi Arabia, and the results demonstrate the effectiveness of the proposed
approach
Dance-the-music : an educational platform for the modeling, recognition and audiovisual monitoring of dance steps using spatiotemporal motion templates
In this article, a computational platform is presented, entitled “Dance-the-Music”, that can be used in a dance educational context to explore and learn the basics of dance steps. By introducing a method based on spatiotemporal motion templates, the platform facilitates to train basic step models from sequentially repeated dance figures performed by a dance teacher. Movements are captured with an optical motion capture system. The teachers’ models can be visualized from a first-person perspective to instruct students how to perform the specific dance steps in the correct manner. Moreover, recognition algorithms-based on a template matching method can determine the quality of a student’s performance in real time by means of multimodal monitoring techniques. The results of an evaluation study suggest that the Dance-the-Music is effective in helping dance students to master the basics of dance figures
08451 Abstracts Collection -- Representation, Analysis and Visualization of Moving Objects
From 02.11. to 07.11.2008, the Dagstuhl Seminar 08451 ``Representation, Analysis and Visualization of Moving Objects \u27\u27 was held
in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
Mapping temporal variables into the NeuCube for improved pattern recognition, predictive modelling, and understanding of stream data.
This paper proposes a new method for an optimized mapping of temporal
variables, describing a temporal stream data, into the recently proposed
NeuCube spiking neural network architecture. This optimized mapping extends the
use of the NeuCube, which was initially designed for spatiotemporal brain data,
to work on arbitrary stream data and to achieve a better accuracy of temporal
pattern recognition, a better and earlier event prediction and a better
understanding of complex temporal stream data through visualization of the
NeuCube connectivity. The effect of the new mapping is demonstrated on three
bench mark problems. The first one is early prediction of patient sleep stage
event from temporal physiological data. The second one is pattern recognition
of dynamic temporal patterns of traffic in the Bay Area of California and the
last one is the Challenge 2012 contest data set. In all cases the use of the
proposed mapping leads to an improved accuracy of pattern recognition and event
prediction and a better understanding of the data when compared to traditional
machine learning techniques or spiking neural network reservoirs with arbitrary
mapping of the variables.Comment: Accepted by IEEE TNNL
Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data
Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour
Self-Organizing Time Map: An Abstraction of Temporal Multivariate Patterns
This paper adopts and adapts Kohonen's standard Self-Organizing Map (SOM) for
exploratory temporal structure analysis. The Self-Organizing Time Map (SOTM)
implements SOM-type learning to one-dimensional arrays for individual time
units, preserves the orientation with short-term memory and arranges the arrays
in an ascending order of time. The two-dimensional representation of the SOTM
attempts thus twofold topology preservation, where the horizontal direction
preserves time topology and the vertical direction data topology. This enables
discovering the occurrence and exploring the properties of temporal structural
changes in data. For representing qualities and properties of SOTMs, we adapt
measures and visualizations from the standard SOM paradigm, as well as
introduce a measure of temporal structural changes. The functioning of the
SOTM, and its visualizations and quality and property measures, are illustrated
on artificial toy data. The usefulness of the SOTM in a real-world setting is
shown on poverty, welfare and development indicators
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