80,112 research outputs found
Class-Agnostic Counting
Nearly all existing counting methods are designed for a specific object
class. Our work, however, aims to create a counting model able to count any
class of object. To achieve this goal, we formulate counting as a matching
problem, enabling us to exploit the image self-similarity property that
naturally exists in object counting problems. We make the following three
contributions: first, a Generic Matching Network (GMN) architecture that can
potentially count any object in a class-agnostic manner; second, by
reformulating the counting problem as one of matching objects, we can take
advantage of the abundance of video data labeled for tracking, which contains
natural repetitions suitable for training a counting model. Such data enables
us to train the GMN. Third, to customize the GMN to different user
requirements, an adapter module is used to specialize the model with minimal
effort, i.e. using a few labeled examples, and adapting only a small fraction
of the trained parameters. This is a form of few-shot learning, which is
practical for domains where labels are limited due to requiring expert
knowledge (e.g. microbiology). We demonstrate the flexibility of our method on
a diverse set of existing counting benchmarks: specifically cells, cars, and
human crowds. The model achieves competitive performance on cell and crowd
counting datasets, and surpasses the state-of-the-art on the car dataset using
only three training images. When training on the entire dataset, the proposed
method outperforms all previous methods by a large margin.Comment: Asian Conference on Computer Vision (ACCV), 201
TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents
To safely and efficiently navigate in complex urban traffic, autonomous
vehicles must make responsible predictions in relation to surrounding
traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and
critical task is to explore the movement patterns of different traffic-agents
and predict their future trajectories accurately to help the autonomous vehicle
make reasonable navigation decision. To solve this problem, we propose a long
short-term memory-based (LSTM-based) realtime traffic prediction algorithm,
TrafficPredict. Our approach uses an instance layer to learn instances'
movements and interactions and has a category layer to learn the similarities
of instances belonging to the same type to refine the prediction. In order to
evaluate its performance, we collected trajectory datasets in a large city
consisting of varying conditions and traffic densities. The dataset includes
many challenging scenarios where vehicles, bicycles, and pedestrians move among
one another. We evaluate the performance of TrafficPredict on our new dataset
and highlight its higher accuracy for trajectory prediction by comparing with
prior prediction methods.Comment: Accepted by AAAI(Oral) 201
Differential Equations Modeling Crowd Interactions
Nonlocal conservation laws are used to describe various realistic instances
of crowd behaviors. First, a basic analytic framework is established through an
"ad hoc" well posedness theorem for systems of nonlocal conservation laws in
several space dimensions interacting non locally with a system of ODEs.
Numerical integrations show possible applications to the interaction of
different groups of pedestrians, and also with other "agents".Comment: 26 pages, 5 figure
Optimal Self-Organization
We present computational and analytical results indicating that systems of
driven entities with repulsive interactions tend to reach an optimal state
associated with minimal interaction and minimal dissipation. Using concepts
from non-equilibrium thermodynamics and game theoretical ideas, we generalize
this finding to an even wider class of self-organizing systems which have the
ability to reach a state of maximal overall ``success''. This principle is
expected to be relevant for driven systems in physics like sheared granular
media, but it is also applicable to biological, social, and economic systems,
for which only a limited number of quantitative principles are available yet.Comment: This is the detailled revised version of a preprint on
``Self-Organised Optimality'' (cond-mat/9903319). For related work see
http://www.theo2.physik.uni-stuttgart.de/helbing.html and
http://angel.elte.hu/~vicsek
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