Farther Than Mirror: Explore Pattern-Compensated Depth of Mirror with Temporal Changes for Video Mirror Detection
Keywords: Video mirror detection, depth estimation, affinity, visual pattern compensation
TL;DR: FTM-Net
Abstract: Current video mirror detection models demonstrate satisfactory performance by analyzing different attributes of mirrors and incorporating temporal information. However, these models still struggle to detect mirrors in complex and dynamic scenarios.
A simple yet critical visual cue is that objects reflected in a mirror appear to be farther away than the mirror itself. Motivated by this observation, we propose to explicitly analyze the Depth of Mirror (DOM) within a video to effectively localize mirrors - DOM refers to distinct perceived distances that make mirror regions appear farther away from their surroundings. Specifically, we devise a novel framework called FTM-Net, which contains two main contributions: a Pattern-Compensated DOM estimation strategy and a Dual-Granularity Affinity module. The Pattern-Compensated DOM estimation strategy uses multiple visual mirror patterns to refine the DOM, enhancing the accuracy of mirror localization in a single image. Furthermore, the Dual-Granularity Affinity module can effectively detect mirrors in video sequences by tracking and integrating DOM changes across frames. Experimental results on a benchmark dataset show that our model significantly outperforms 18 state-of-the-art methods in the video mirror detection task.
Supplementary Material: zip
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 983
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