Abstract
Mirror and glass are ubiquitous materials that heavily influence the transmission of indoor 5G signals with extremely high frequency. The existing vision system always tends to neglect them or misdiagnose them, which is unsuitable for 5G signal analysis in the 3D indoor environment. This paper proposes key points trajectory distinction and predicted-real frames distinction to detect mirror and glass regions in video sequences. Firstly, key points trajectory is used to extract the special motion information of reflection in mirror and glass region. Secondly, predicted and real frames distinction is used to remove the wrong detection region at the pixel level. Extensive experiments demonstrate that the proposed method achieves 40 - 50 % accuracy higher than another related state-of-the-art method for mirror and glass detection in the general 3D living environment.
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