Continuous Graph Partitioning for Camera Network Surveillance*

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

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A Genetic Local Search Algorithm for the Graph Partitioning Problem with Cardinality Constraints

13th IFAC Symposium on Information Control Problems in Manufacturing ◽

10.3182/20090603-3-ru-2001.00335 ◽

2009 ◽

Author(s):

Kochetov, Yuri

Keyword(s):

Local Search ◽

Graph Partitioning ◽

Search Algorithm ◽

Local Search Algorithm ◽

Cardinality Constraints ◽

Graph Partitioning Problem ◽

Partitioning Problem ◽

Genetic Local Search

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FINAL VERSION.pdf

10.36227/techrxiv.10252637.v1 ◽

2019 ◽

Author(s):

Nasir Saeed ◽

Mohamed-Slim Alouini ◽

Tareq Y. Al-Naffouri

Keyword(s):

Graph Partitioning ◽

Three Dimensional ◽

Smart Objects ◽

Localization Technique ◽

Optical Internet ◽

Spectral Graph ◽

Accurate Localization ◽

Spectral Graph Partitioning ◽

Link Connectivity

<div>Localization is a fundamental task for optical internet</div><div>of underwater things (O-IoUT) to enable various applications</div><div>such as data tagging, routing, navigation, and maintaining link connectivity. The accuracy of the localization techniques for OIoUT greatly relies on the location of the anchors. Therefore, recently localization techniques for O-IoUT which optimize the anchor’s location are proposed. However, optimization of anchors location for all the smart objects in the network is not a useful solution. Indeed, in a network of densely populated smart objects, the data collected by some sensors are more valuable than the data collected from other sensors. Therefore, in this paper, we propose a three-dimensional accurate localization technique by optimizing the anchor’s location for a set of smart objects. Spectral graph partitioning is used to select the set of valuable</div><div>sensors.</div>

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Mathematics of networks

10.1093/oso/9780198805090.003.0006 ◽

2018 ◽

Author(s):

Mark Newman

Keyword(s):

Random Walks ◽

Adjacency Matrix ◽

Graph Partitioning ◽

Dynamic Networks ◽

Graph Laplacian ◽

Network Visualization ◽

Final Part ◽

Bipartite Networks ◽

Component Structure ◽

Basic Properties

An introduction to the mathematical tools used in the study of networks. Topics discussed include: the adjacency matrix; weighted, directed, acyclic, and bipartite networks; multilayer and dynamic networks; trees; planar networks. Some basic properties of networks are then discussed, including degrees, density and sparsity, paths on networks, component structure, and connectivity and cut sets. The final part of the chapter focuses on the graph Laplacian and its applications to network visualization, graph partitioning, the theory of random walks, and other problems.

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