15,406 research outputs found
Ensemble of Different Approaches for a Reliable Person Re-identification System
An ensemble of approaches for reliable person re-identification is proposed in this paper. The proposed ensemble is built combining widely used person re-identification systems using different color spaces and some variants of state-of-the-art approaches that are proposed in this paper. Different descriptors are tested, and both texture and color features are extracted from the images; then the different descriptors are compared using different distance measures (e.g., the Euclidean distance, angle, and the Jeffrey distance). To improve performance, a method based on skeleton detection, extracted from the depth map, is also applied when the depth map is available. The proposed ensemble is validated on three widely used datasets (CAVIAR4REID, IAS, and VIPeR), keeping the same parameter set of each approach constant across all tests to avoid overfitting and to demonstrate that the proposed system can be considered a general-purpose person re-identification system. Our experimental results show that the proposed system offers significant improvements over baseline approaches. The source code used for the approaches tested in this paper will be available at https://www.dei.unipd.it/node/2357 and http://robotics.dei.unipd.it/reid/
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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