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Co-occurrence spatial-temporal model for adaptive background initialization in high-dynamic complex scenes
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AI
DOI：10.1016/j.image.2023.117056.png)
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Abstract
En 中文
Background information is an important aspect of pre-processing for advanced applications in computer vision. The literature has made rapid progress in background initialization. However, background initialization still suffers from high-dynamic complex scenes, such as illumination change, background motion, or camera jitter. Therefore, this study presents a novel Co-occurrence Spatial-Temporal (CoST) model for background initialization in high-dynamic complex scenes. CoST achieves a spatial-temporal model through a co-occurrence pixel-block structure. The proposed approach extracts the spatial-temporal information of pixels to self-adaptively generate the background without the influence of high-dynamic complex scenes. The efficiency of CoST is verified through experimental results compared with state-of-the-art algorithms. The source code of CoST is available online at: https://github.com/HelloMrDeng/CoST.git.
Keywords:
Background initialization
Spatial-Temporal model
Foreground detection
High-dynamic complex scene
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