25,300 research outputs found
Person re-identification via efficient inference in fully connected CRF
In this paper, we address the problem of person re-identification problem,
i.e., retrieving instances from gallery which are generated by the same person
as the given probe image. This is very challenging because the person's
appearance usually undergoes significant variations due to changes in
illumination, camera angle and view, background clutter, and occlusion over the
camera network. In this paper, we assume that the matched gallery images should
not only be similar to the probe, but also be similar to each other, under
suitable metric. We express this assumption with a fully connected CRF model in
which each node corresponds to a gallery and every pair of nodes are connected
by an edge. A label variable is associated with each node to indicate whether
the corresponding image is from target person. We define unary potential for
each node using existing feature calculation and matching techniques, which
reflect the similarity between probe and gallery image, and define pairwise
potential for each edge in terms of a weighed combination of Gaussian kernels,
which encode appearance similarity between pair of gallery images. The specific
form of pairwise potential allows us to exploit an efficient inference
algorithm to calculate the marginal distribution of each label variable for
this dense connected CRF. We show the superiority of our method by applying it
to public datasets and comparing with the state of the art.Comment: 7 pages, 4 figure
Learning to rank in person re-identification with metric ensembles
We propose an effective structured learning based approach to the problem of
person re-identification which outperforms the current state-of-the-art on most
benchmark data sets evaluated. Our framework is built on the basis of multiple
low-level hand-crafted and high-level visual features. We then formulate two
optimization algorithms, which directly optimize evaluation measures commonly
used in person re-identification, also known as the Cumulative Matching
Characteristic (CMC) curve. Our new approach is practical to many real-world
surveillance applications as the re-identification performance can be
concentrated in the range of most practical importance. The combination of
these factors leads to a person re-identification system which outperforms most
existing algorithms. More importantly, we advance state-of-the-art results on
person re-identification by improving the rank- recognition rates from
to on the iLIDS benchmark, to on the PRID2011
benchmark, to on the VIPeR benchmark, to on the
CUHK01 benchmark and to on the CUHK03 benchmark.Comment: 10 page
A Watch-List Based Classification System
Watch-list-based classification and verification is advantageous in a variety of surveillance applications. In this thesis, we present an approach for verifying if a query image lies in a predefined set of target samples (the watch-list) or not. This approach is particularly useful at identifying a small set of target subjects and therefore can render high levels of accuracy. Further, this approach can also be extended to identify the query image exactly out of the target samples. The three- stages approach proposed here consists of using a combination of color and texture features to represent the image and further using, Kernel Partial Least Squares for dimensionality reduction followed by a classifier. This approach provides improved accuracy as shown by experiments on two datasets
A Novel Multi-Color Feature Selection Method for Person Re-identification
In this paper, a novel multi-color feature selection method is proposed for person re-identification. Firstly, multi-color features, which consisting of HSV, LAB, RGB and nRnG color features, were extracted and concatenated into a whole feature vector. Then the D-optimal Partial Least Squares feature selection method was adopted to select an optimal feature subset that could minimize the variance of the regression model. Finally, an asymmetric distance model for similarity matching was utilized to observe distinctive features from a different perspective. Experimental results show that rank 1 performance of the proposed method were 48.67%, 63.12% and 65.04% respectively on the VIPeR, Prid_450s and CUHK01 databases, which have achieved state-of-art performances
Review on Human Re-identification with Multiple Cameras
Human re-identification is the core task in most surveillance systems and it is aimed at matching human pairs from different non-overlapping cameras. There are several challenging issues that need to be overcome to achieve reidentification, such as overcoming the variations in viewpoint, pose, image resolution, illumination and occlusion. In this study, we review existing works in human re-identification task. Advantages and limitations of recent works are discussed. At the end, this paper suggests some future research directions for human re-identification
Structured learning of metric ensembles with application to person re-identification
Matching individuals across non-overlapping camera networks, known as person
re-identification, is a fundamentally challenging problem due to the large
visual appearance changes caused by variations of viewpoints, lighting, and
occlusion. Approaches in literature can be categoried into two streams: The
first stream is to develop reliable features against realistic conditions by
combining several visual features in a pre-defined way; the second stream is to
learn a metric from training data to ensure strong inter-class differences and
intra-class similarities. However, seeking an optimal combination of visual
features which is generic yet adaptive to different benchmarks is a unsoved
problem, and metric learning models easily get over-fitted due to the scarcity
of training data in person re-identification. In this paper, we propose two
effective structured learning based approaches which explore the adaptive
effects of visual features in recognizing persons in different benchmark data
sets. Our framework is built on the basis of multiple low-level visual features
with an optimal ensemble of their metrics. We formulate two optimization
algorithms, CMCtriplet and CMCstruct, which directly optimize evaluation
measures commonly used in person re-identification, also known as the
Cumulative Matching Characteristic (CMC) curve.Comment: 16 pages. Extended version of "Learning to Rank in Person
Re-Identification With Metric Ensembles", at
http://www.cv-foundation.org/openaccess/content_cvpr_2015/html/Paisitkriangkrai_Learning_to_Rank_2015_CVPR_paper.html.
arXiv admin note: text overlap with arXiv:1503.0154
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
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