Distributed Data Association in Smart Camera Networks via Dual Decomposition
Jiuqing Wan, Yuting Nie, Li Liu

TL;DR
This paper introduces distributed algorithms based on dual decomposition for data association in smart camera networks, enabling local processing and efficient consensus among cameras for large-scale visual surveillance.
Contribution
It formulates data association as an Integer Programming problem and proposes two novel distributed algorithms, L-DD and Q-DD, that are flexible and incorporate various feature matching techniques.
Findings
Algorithms achieve high accuracy in real-world datasets.
Methods demonstrate superior speed and scalability.
Framework is adaptable to different feature extraction methods.
Abstract
One of the fundamental requirements for visual surveillance using smart camera networks is the correct association of each persons observations generated on different cameras. Recently, distributed data association that involves only local information processing on each camera node and mutual information exchanging between neighboring cameras has attracted many research interests due to its superiority in large scale applications. In this paper, we formulate the problem of data association in smart camera networks as an Integer Programming problem by introducing a set of linking variables, and propose two distributed algorithms, namely L-DD and Q-DD, to solve the Integer Programming problem using dual decomposition technique. In our algorithms, the original IP problem is decomposed into several sub-problems, which can be solved locally and efficiently on each smart camera, and then…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Infrared Target Detection Methodologies · Advanced Data Compression Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
