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Background motion had an additive, negative impact on performance.
We first estimate the global background motion from to.
None of the other subject groups showed any influence of background motion on spatial judgments.
The authors improved their trajectories by performing irrelevant background motion removal using warped flow [8].
Thereby, the background motion is assumed to be the dominant motion in the scene.
With no background motion, RNLM with and without BMA perform about the same.
This algorithm can minimize the color variations generated by background motion.
Ko et al. [21] presented a background model that differentiates between background motion and foreground objects.
In Experiment 2 the modifiability of these distortions was tested by the use of slow visual background motion.
Therefore, the key of motion detection in dynamic scenes is to distinguish the background motion from the target motion.
On the contrary, the second video has a cluttered background motion because the grass and branches present irregular motion.
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