841 research outputs found
Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector
Multiple object tracking (MOT) in urban traffic aims to produce the
trajectories of the different road users that move across the field of view
with different directions and speeds and that can have varying appearances and
sizes. Occlusions and interactions among the different objects are expected and
common due to the nature of urban road traffic. In this work, a tracking
framework employing classification label information from a deep learning
detection approach is used for associating the different objects, in addition
to object position and appearances. We want to investigate the performance of a
modern multiclass object detector for the MOT task in traffic scenes. Results
show that the object labels improve tracking performance, but that the output
of object detectors are not always reliable.Comment: 13th International Symposium on Visual Computing (ISVC
Robust Person Tracking Algorithm Based on Convolutional Neural Network for Indoor Video Surveillance Systems
In this paper, we present an algorithm for multi person tracking in indoor surveillance systems based on tracking-by-detection approach. Convolutional Neural Networks (CNNs) for detection and tracking both are used. CNN Yolov3 has been utilized as detector. Person features extraction is performed based on modified CNN ResNet. Proposed architecture includes 29 convolutional and one fully connected layer. Hungarian algorithm is applied for objects association. After that object visibility in the frame is determined based on CNN and color features. For algorithm evaluation prepared videos that was labeled and tested using MOT evaluation metric. The proposed algorithm efficiency is illustrated and confirmed by our experimental results
Measuring Global Similarity between Texts
We propose a new similarity measure between texts which, contrary to the
current state-of-the-art approaches, takes a global view of the texts to be
compared. We have implemented a tool to compute our textual distance and
conducted experiments on several corpuses of texts. The experiments show that
our methods can reliably identify different global types of texts.Comment: Submitted to SLSP 201
Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation
Unsupervised domain adaptation has caught appealing attentions as it facilitates the unlabeled target learning by borrowing existing well-established source domain knowledge. Recent practice on domain adaptation manages to extract effective features by incorporating the pseudo labels for the target domain to better solve cross-domain distribution divergences. However, existing approaches separate target label optimization and domain-invariant feature learning as different steps. To address that issue, we develop a novel Graph Adaptive Knowledge Transfer (GAKT) model to jointly optimize target labels and domain-free features in a unified framework. Specifically, semi-supervised knowledge adaptation and label propagation on target data are coupled to benefit each other, and hence the marginal and conditional disparities across different domains will be better alleviated. Experimental evaluation on two cross-domain visual datasets demonstrates the effectiveness of our designed approach on facilitating the unlabeled target task learning, compared to the state-of-the-art domain adaptation approaches
Adaptive Evolutionary Clustering
In many practical applications of clustering, the objects to be clustered
evolve over time, and a clustering result is desired at each time step. In such
applications, evolutionary clustering typically outperforms traditional static
clustering by producing clustering results that reflect long-term trends while
being robust to short-term variations. Several evolutionary clustering
algorithms have recently been proposed, often by adding a temporal smoothness
penalty to the cost function of a static clustering method. In this paper, we
introduce a different approach to evolutionary clustering by accurately
tracking the time-varying proximities between objects followed by static
clustering. We present an evolutionary clustering framework that adaptively
estimates the optimal smoothing parameter using shrinkage estimation, a
statistical approach that improves a naive estimate using additional
information. The proposed framework can be used to extend a variety of static
clustering algorithms, including hierarchical, k-means, and spectral
clustering, into evolutionary clustering algorithms. Experiments on synthetic
and real data sets indicate that the proposed framework outperforms static
clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox
available at http://tbayes.eecs.umich.edu/xukevin/affec
Non-zero-sum Dresher inspection games
Dedicated to the memory of Eckhard Hopnger (1941{1990
k is the Magic Number -- Inferring the Number of Clusters Through Nonparametric Concentration Inequalities
Most convex and nonconvex clustering algorithms come with one crucial
parameter: the in -means. To this day, there is not one generally
accepted way to accurately determine this parameter. Popular methods are simple
yet theoretically unfounded, such as searching for an elbow in the curve of a
given cost measure. In contrast, statistically founded methods often make
strict assumptions over the data distribution or come with their own
optimization scheme for the clustering objective. This limits either the set of
applicable datasets or clustering algorithms. In this paper, we strive to
determine the number of clusters by answering a simple question: given two
clusters, is it likely that they jointly stem from a single distribution? To
this end, we propose a bound on the probability that two clusters originate
from the distribution of the unified cluster, specified only by the sample mean
and variance. Our method is applicable as a simple wrapper to the result of any
clustering method minimizing the objective of -means, which includes
Gaussian mixtures and Spectral Clustering. We focus in our experimental
evaluation on an application for nonconvex clustering and demonstrate the
suitability of our theoretical results. Our \textsc{SpecialK} clustering
algorithm automatically determines the appropriate value for , without
requiring any data transformation or projection, and without assumptions on the
data distribution. Additionally, it is capable to decide that the data consists
of only a single cluster, which many existing algorithms cannot
Cavity cooling of a single atom
All conventional methods to laser-cool atoms rely on repeated cycles of
optical pumping and spontaneous emission of a photon by the atom. Spontaneous
emission in a random direction is the dissipative mechanism required to remove
entropy from the atom. However, alternative cooling methods have been proposed
for a single atom strongly coupled to a high-finesse cavity; the role of
spontaneous emission is replaced by the escape of a photon from the cavity.
Application of such cooling schemes would improve the performance of atom
cavity systems for quantum information processing. Furthermore, as cavity
cooling does not rely on spontaneous emission, it can be applied to systems
that cannot be laser-cooled by conventional methods; these include molecules
(which do not have a closed transition) and collective excitations of Bose
condensates, which are destroyed by randomly directed recoil kicks. Here we
demonstrate cavity cooling of single rubidium atoms stored in an intracavity
dipole trap. The cooling mechanism results in extended storage times and
improved localization of atoms. We estimate that the observed cooling rate is
at least five times larger than that produced by free-space cooling methods,
for comparable excitation of the atom
Tracking Target Signal Strengths on a Grid using Sparsity
Multi-target tracking is mainly challenged by the nonlinearity present in the
measurement equation, and the difficulty in fast and accurate data association.
To overcome these challenges, the present paper introduces a grid-based model
in which the state captures target signal strengths on a known spatial grid
(TSSG). This model leads to \emph{linear} state and measurement equations,
which bypass data association and can afford state estimation via
sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of
the novel model, two types of sparsity-cognizant TSSG-KF trackers are
developed: one effects sparsity through -norm regularization, and the
other invokes sparsity as an extra measurement. Iterative extended KF and
Gauss-Newton algorithms are developed for reduced-complexity tracking, along
with accurate error covariance updates for assessing performance of the
resultant sparsity-aware state estimators. Based on TSSG state estimates, more
informative target position and track estimates can be obtained in a follow-up
step, ensuring that track association and position estimation errors do not
propagate back into TSSG state estimates. The novel TSSG trackers do not
require knowing the number of targets or their signal strengths, and exhibit
considerably lower complexity than the benchmark hidden Markov model filter,
especially for a large number of targets. Numerical simulations demonstrate
that sparsity-cognizant trackers enjoy improved root mean-square error
performance at reduced complexity when compared to their sparsity-agnostic
counterparts.Comment: Submitted to IEEE Trans. on Signal Processin
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