3,099 research outputs found
Person Re-identification Using Clustering Ensemble Prototypes
Abstract. This paper presents an appearance-based model to deal with the person re-identification problem. Usually in a crowded scene, it is ob-served that, the appearances of most people are similar with regard to the combination of attire. In such situation it is a difficult task to distin-guish an individual from a group of alike looking individuals and yields an ambiguity in recognition for re-identification. The proper organiza-tion of the individuals based on the appearance characteristics leads to recognize the target individual by comparing with a particular group of similar looking individuals. To reconstruct a group of individual accord-ing to their appearance is a crucial task for person re-identification. In this work we focus on unsupervised based clustering ensemble approach for discovering prototypes where each prototype represents similar set of gallery image instances. The formation of each prototype depends upon the appearance characteristics of gallery instances. The estimation of k-NN classifier is employed to specify a prototype to a given probe image. The similarity measure computation is performed between the probe and a subset of gallery images, that shares the same prototype with the probe and thus reduces the number of comparisons. Re-identification perfor-mance on benchmark datasets are presented using cumulative matching characteristic (CMC) curves.
Multiple Instance Learning: A Survey of Problem Characteristics and Applications
Multiple instance learning (MIL) is a form of weakly supervised learning
where training instances are arranged in sets, called bags, and a label is
provided for the entire bag. This formulation is gaining interest because it
naturally fits various problems and allows to leverage weakly labeled data.
Consequently, it has been used in diverse application fields such as computer
vision and document classification. However, learning from bags raises
important challenges that are unique to MIL. This paper provides a
comprehensive survey of the characteristics which define and differentiate the
types of MIL problems. Until now, these problem characteristics have not been
formally identified and described. As a result, the variations in performance
of MIL algorithms from one data set to another are difficult to explain. In
this paper, MIL problem characteristics are grouped into four broad categories:
the composition of the bags, the types of data distribution, the ambiguity of
instance labels, and the task to be performed. Methods specialized to address
each category are reviewed. Then, the extent to which these characteristics
manifest themselves in key MIL application areas are described. Finally,
experiments are conducted to compare the performance of 16 state-of-the-art MIL
methods on selected problem characteristics. This paper provides insight on how
the problem characteristics affect MIL algorithms, recommendations for future
benchmarking and promising avenues for research
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
TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning with Structure-Trajectory Prompted Reconstruction for Person Re-Identification
Person re-identification (re-ID) via 3D skeleton data is an emerging topic
with prominent advantages. Existing methods usually design skeleton descriptors
with raw body joints or perform skeleton sequence representation learning.
However, they typically cannot concurrently model different body-component
relations, and rarely explore useful semantics from fine-grained
representations of body joints. In this paper, we propose a generic
Transformer-based Skeleton Graph prototype contrastive learning (TranSG)
approach with structure-trajectory prompted reconstruction to fully capture
skeletal relations and valuable spatial-temporal semantics from skeleton graphs
for person re-ID. Specifically, we first devise the Skeleton Graph Transformer
(SGT) to simultaneously learn body and motion relations within skeleton graphs,
so as to aggregate key correlative node features into graph representations.
Then, we propose the Graph Prototype Contrastive learning (GPC) to mine the
most typical graph features (graph prototypes) of each identity, and contrast
the inherent similarity between graph representations and different prototypes
from both skeleton and sequence levels to learn discriminative graph
representations. Last, a graph Structure-Trajectory Prompted Reconstruction
(STPR) mechanism is proposed to exploit the spatial and temporal contexts of
graph nodes to prompt skeleton graph reconstruction, which facilitates
capturing more valuable patterns and graph semantics for person re-ID.
Empirical evaluations demonstrate that TranSG significantly outperforms
existing state-of-the-art methods. We further show its generality under
different graph modeling, RGB-estimated skeletons, and unsupervised scenarios.Comment: Accepted by CVPR 2023. Codes are available at
https://github.com/Kali-Hac/TranSG. Supplemental material is included in the
conference proceeding
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