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Real-Time Approach for Adaptive Object Segmentation in Time-of-Flight Sensors

机译：飞行时间传感器中自适应对象分割的实时方法

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摘要

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.
机译：在本文中，提出了一种高速，自适应深度分段方法，这导致在实时跟踪应用中应用时对电流采用的分割算法的优异性能。由于其性能慢，因此难以在实时实施现有的分割方法，从而提高其运行时它们更好地适用于实时方法，即跟踪。该方法利用范围图像的深度分布来分割感兴趣的对象，而不具有关于场景的先验知识。在不同的条件下，这种方法已经在不受约束的环境中使用了无约束环境中的实际数据进行了测试。实验结果表明了速度效率，以及所提出的技术的鲁棒性。

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