Particle filter framework for salient object detection in videos


Salient object detection in videos is difficult because of the competing motion within the background, ensuing from camera tracking an object of interest, or motion of objects within the foreground. The authors present a fast methodology to detect salient video objects using particle filters, that are guided by spatio-temporal saliency maps and color feature with the flexibility to quickly pass though false detections. The proposed technique for generating spatial and motion saliency maps is predicated on comparing local features with dominant options gift in the frame. A region is marked salient if there's a large distinction between local and dominant options. For spatial saliency, hue and saturation features are used, whereas for motion saliency, optical flow vectors are used as features. Experimental results on customary datasets for video segmentation and for saliency detection show superior performance over state-of-the-art strategies.

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