9,862 research outputs found
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
Person re-identification is an important technique towards automatic search
of a person's presence in a surveillance video. Two fundamental problems are
critical for person re-identification, feature representation and metric
learning. An effective feature representation should be robust to illumination
and viewpoint changes, and a discriminant metric should be learned to match
various person images. In this paper, we propose an effective feature
representation called Local Maximal Occurrence (LOMO), and a subspace and
metric learning method called Cross-view Quadratic Discriminant Analysis
(XQDA). The LOMO feature analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation against viewpoint
changes. Besides, to handle illumination variations, we apply the Retinex
transform and a scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional subspace by
cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is
learned on the derived subspace. We also present a practical computation method
for XQDA, as well as its regularization. Experiments on four challenging person
re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show
that the proposed method improves the state-of-the-art rank-1 identification
rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and
extracted features please visit
http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda
VID-Trans-ReID: Enhanced Video Transformers for Person Re-identification
Video-based person Re-identification (Re-ID) has received increasing attention recently due to its important role within surveillance video analysis. Video-based Re- ID expands upon earlier image-based methods by extracting person features temporally across multiple video image frames. The key challenge within person Re-ID is extracting a robust feature representation that is invariant to the challenges of pose and illumination variation across multiple camera viewpoints. Whilst most contemporary methods use a CNN based methodology, recent advances in vision transformer (ViT) architectures boost fine-grained feature discrimination via the use of both multi-head attention without any loss of feature robustness. To specifically enable ViT architectures to effectively address the challenges of video person Re-ID, we propose two novel modules constructs, Temporal Clip Shift and Shuffled (TCSS) and Video Patch Part Feature (VPPF), that boost the robustness of the resultant Re-ID feature representation. Furthermore, we combine our proposed approach with current best practices spanning both image and video based Re-ID including camera view embedding. Our proposed approach outperforms existing state-of-the-art work on the MARS, PRID2011, and iLIDS-VID Re-ID benchmark datasets achieving 96.36%, 96.63%, 94.67% rank-1 accuracy respectively and achieving 90.25% mAP on MARS
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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