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Accurate Object Segmentation for Video Sequences via Temporal-Spatial-Frequency Saliency Model

In practical applications of video object segmentation, a major challenge is how to increase the accuracy and robustness of complex dynamic scenes and achieve positive scalability under camera displacement conditions. This paper proposes a novel approach to object segmentation by combining information in temporal, spatial, and frequency domains to realize complementary superiority. Four components, which are motion, color, luminance, and spectral residual from three domains, are applied to structure the temporal-spatial-frequency saliency (TSFS) model. A determining rule is defined to merge the components into a final model. The proposed model is evaluated on two representative video sequences datasets. The experimental results indicate that the model is more accurate, robust, and effective than other state-of-the-art methods, and can satisfy the requirements of segmenting the object in complex dynamic scenes and large camera displacement conditions.