1,083 research outputs found
Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks
This work addresses the problem of vehicle identification through
non-overlapping cameras. As our main contribution, we introduce a novel dataset
for vehicle identification, called Vehicle-Rear, that contains more than three
hours of high-resolution videos, with accurate information about the make,
model, color and year of nearly 3,000 vehicles, in addition to the position and
identification of their license plates. To explore our dataset we design a
two-stream CNN that simultaneously uses two of the most distinctive and
persistent features available: the vehicle's appearance and its license plate.
This is an attempt to tackle a major problem: false alarms caused by vehicles
with similar designs or by very close license plate identifiers. In the first
network stream, shape similarities are identified by a Siamese CNN that uses a
pair of low-resolution vehicle patches recorded by two different cameras. In
the second stream, we use a CNN for OCR to extract textual information,
confidence scores, and string similarities from a pair of high-resolution
license plate patches. Then, features from both streams are merged by a
sequence of fully connected layers for decision. In our experiments, we
compared the two-stream network against several well-known CNN architectures
using single or multiple vehicle features. The architectures, trained models,
and dataset are publicly available at https://github.com/icarofua/vehicle-rear
PPF - A Parallel Particle Filtering Library
We present the parallel particle filtering (PPF) software library, which
enables hybrid shared-memory/distributed-memory parallelization of particle
filtering (PF) algorithms combining the Message Passing Interface (MPI) with
multithreading for multi-level parallelism. The library is implemented in Java
and relies on OpenMPI's Java bindings for inter-process communication. It
includes dynamic load balancing, multi-thread balancing, and several
algorithmic improvements for PF, such as input-space domain decomposition. The
PPF library hides the difficulties of efficient parallel programming of PF
algorithms and provides application developers with the necessary tools for
parallel implementation of PF methods. We demonstrate the capabilities of the
PPF library using two distributed PF algorithms in two scenarios with different
numbers of particles. The PPF library runs a 38 million particle problem,
corresponding to more than 1.86 GB of particle data, on 192 cores with 67%
parallel efficiency. To the best of our knowledge, the PPF library is the first
open-source software that offers a parallel framework for PF applications.Comment: 8 pages, 8 figures; will appear in the proceedings of the IET Data
Fusion & Target Tracking Conference 201
Many-to-Many Graph Matching: a Continuous Relaxation Approach
Graphs provide an efficient tool for object representation in various
computer vision applications. Once graph-based representations are constructed,
an important question is how to compare graphs. This problem is often
formulated as a graph matching problem where one seeks a mapping between
vertices of two graphs which optimally aligns their structure. In the classical
formulation of graph matching, only one-to-one correspondences between vertices
are considered. However, in many applications, graphs cannot be matched
perfectly and it is more interesting to consider many-to-many correspondences
where clusters of vertices in one graph are matched to clusters of vertices in
the other graph. In this paper, we formulate the many-to-many graph matching
problem as a discrete optimization problem and propose an approximate algorithm
based on a continuous relaxation of the combinatorial problem. We compare our
method with other existing methods on several benchmark computer vision
datasets.Comment: 1
Visually guided grasping to study teleprogrammation within the BAROCO testbed
This paper describes vision functionalities required in future orbital laboratories; in such systems, robots will be needed in order to execute the on-board scientific experiments or servicing and maintenance tasks under the remote control of ground operators. For this sake, ESA has proposed a robotic configuration called EMATS; a testbed has been developed by ESTEC in order to evaluate the potentialities of EMATS-like robot to execute scientific tasks in automatic mode. For the same context, CNES develops the BAROCO testbed to investigate remote control and teleprogrammation, in which high level primitives like 'Pick Object A' are provided as basic primitives. In nominal situations, the system has an a priori knowledge about the position of all objects. These positions are not very accurate, but this knowledge is sufficient in order to predict the position of the object which must be grasped, with respect to the manipulator frame. Vision is required in order to insure a correct grasping and to guarantee a good accuracy for the following operations. We describe our results about a visually guided grasping of static objects. It seems to be a very classical problem, and a lot of results are available. But, in many cases, it lacks a realistic evaluation of the accuracy, because such an evaluation requires tedious experiments. We propose several results about calibration of the experimental testbed, recognition algorithms required to locate a 3D polyhedral object, and the grasping itself
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