Region-Based Multiscale Spatiotemporal Saliency for Video

TL;DR

This paper introduces a novel region-based multiscale spatiotemporal saliency detection method for videos that combines static and dynamic features across spatial and temporal domains, improving salient object detection.

Contribution

The proposed method uniquely integrates multiscale segmentation, adaptive temporal windows, and combined static-dynamic features for enhanced video saliency detection.

Findings

Outperforms existing state-of-the-art methods on benchmark datasets

Effectively combines spatial and temporal saliency cues

Maintains temporal consistency through adaptive motion-based windows

Abstract

Detecting salient objects from a video requires exploiting both spatial and temporal knowledge included in the video. We propose a novel region-based multiscale spatiotemporal saliency detection method for videos, where static features and dynamic features computed from the low and middle levels are combined together. Our method utilizes such combined features spatially over each frame and, at the same time, temporally across frames using consistency between consecutive frames. Saliency cues in our method are analyzed through a multiscale segmentation model, and fused across scale levels, yielding to exploring regions efficiently. An adaptive temporal window using motion information is also developed to combine saliency values of consecutive frames in order to keep temporal consistency across frames. Performance evaluation on several popular benchmark datasets validates that our method outperforms existing state-of-the-arts.