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In our approach, the frame-level hand presence or absence is observed based on motion saliency which is particular in the egocentric view.
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Features based on motion and periodicity characteristics.
Extending our previous work [29], we propose a novel method to measure visual saliency based on biologically plausible saliency mechanisms with a reasonable mathematical formulation.
So it usually leads to low accuracy using the conventional saliency algorithms, i.e., saliency algorithm based on regions and saliency algorithm based on frequency, to calculate the salient map because many small planes are filtered out as background chip.
Accordingly, we developed a content-motion-aware motion estimation based on the motion-level detection.
This implies that prediction is based on object motion rather than retinal motion.
The driftage motion calculation is based on rigid motion analysis.
Meanwhile, two groups of global object locations are obtained based on the saliency detection methods and a segmentation method.
Two key components are fine-tuning the DNNs with an objective function based on the saliency evaluation metrics, and integrating information at different image scales.
In order to better adapt a CNN model into the saliency task, we redesign the network architecture based on typical saliency datasets, which is relatively small-scale compared to ImageNet.
Then the semantic region-of-interest (ROI) is extracted based on the saliency maps of SVA.
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