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In this paper, a high-speed, adaptive depth segmentation method is proposed, which results in superior performance over current employed segmentation algorithms when applied in real-time tracking applications. Existing segmentation methods are difficult to implement in real-time due to their slow performance, whereby enhancing their run-time they are better applicable in real-time approaches, i.e., tracking. The proposed method leverages the depth distributions of range images for segmentation of objects of interest, without having a priori knowledge about the scene. This approach has been tested with real data in unconstrained environments, under varying conditions. The experimental results demonstrate the speed efficiency, as well as robustness of the proposed technique.