Depth Motion Detection—A Novel RS-Trigger Temporal Logic based Method

Abstract

Recently, depth data is widely used in computer vision applications such as detection and tracking, which shows great promises in complicated environments due to its complementary natures to RGB data. However, previous works mostly use depth as an auxiliary cue of RGB data and overlook its inherent advantage on motion detection. Intrinsically different from RGB data, points in depth map essentially represents 3-D positions in the world, so depth video represents the variation of these “positions,” which is motion. Motivated by this, we proposed a novel motion detection scheme based on RS-Trigger temporal logic which best fits nature of depth data on motion detection. The proposed algorithm can fast detect motion regions in the scene without statistics of background and prior knowledge of objects to detect. In following refinement modules, a depth-invariant density-constant projection is proposed which contributes to a fast spatial clustering and accurate segmentation, for it transforms dense 3-D points cloud to depth-invariant 2-D map with density-constance, not only it overcomes depth-dependent sampling of depth sensor, but also overcomes the common `scale problem' in 2-D image analysis, which makes it easy to set system parameters to de-noise and pop-out motion regions. Experimental results validate its effectiveness and efficiency.