Abstract
The Codebook model is one of the popular real-time models for background subtraction. In this paper, we first extend it from traditional Red-Green-Blue (RGB) color model to multispectral sequences. A self-adaptive mechanism is then designed based on the statistical information extracted from the data themselves, with which the performance has been improved, in addition to saving time and effort to search for the appropriate parameters. Furthermore, the Spectral Information Divergence is introduced to evaluate the spectral distance between the current and reference vectors, together with the Brightness and Spectral Distortion. Experiments on five multispectral sequences with different challenges have shown that the multispectral self-adaptive Codebook model is more capable of detecting moving objects than the corresponding RGB sequences. The proposed research framework opens a door for future works for applying multispectral sequences in moving object detection.
Highlights
Moving object detection is often the first step in video processing applications, such as transportation, security and video surveillance
We have proposed a new framework for background subtraction by investigating the advantages of multispectral sequences with the Codebook model
The original Codebook algorithm is adapted to multispectral sequences
Summary
Moving object detection is often the first step in video processing applications, such as transportation, security and video surveillance. A widely used approach for extracting moving objects from the background in the presence of static cameras is detection by background subtraction. Connaissance et Intelligence Artificielle Distribuées (CIAD), University Bourgogne Franche-Comté, UTBM, Enhanced Codebook Model and Fusion for Object Detection with Multispectral Images. In Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Poitiers, France, 24–27 September 2018; pp. Experiments on five multispectral sequences with different challenges have shown that the multispectral self-adaptive Codebook model is more capable of detecting moving objects than the corresponding RGB sequences. Most background subtraction techniques share a common denominator: they make the assumption that the observed video sequence is made of a static background, in front of which moving objects, called foreground, are observed [2]. In Proceedings of the International Conference on Advanced Concepts for Intelligent Vision.
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