Actionable Saliency Detection: Independent Motion Detection Without Independent Motion Estimation

Actionable Saliency Detection: Independent Motion Detection Without Independent Motion Estimation

G. Georgiadis A. Ayvaci S. Soatto

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

​We present a model and an algorithm to detect salient regions in video taken from a moving camera. In particular, we
are interested in capturing small objects that move independently in the scene, such as vehicles and people as seen from
aerial or ground vehicles. Many of the scenarios of interest challenge existing schemes based on background subtraction
(background motion too complex), multi-body motion estimation (insufficient parallax), and occlusion detection
(uniformly textured background regions). We adopt a robust statistical inference approach to simultaneously estimate a
maximally reduced regressor, and select regions that violate the null hypothesis (co-visibility under an epipolar domain
deformation) as “salient”. We show that our algorithm can perform even in the absence of camera calibration information:
while the resulting motion estimates would be incorrect, the partition of the domain into salient vs. non-salient is unaffected. We demonstrate our algorithm on video footage from helicopters, airplanes, and ground vehicles.​

 

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