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Distribution majorization of Corner Points by Reinforcement Learning for Moving Object Detection

机译：通过强化学习进行运动目标检测的角点分布最大化

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Corner points play an important role in moving object detection, especially in the case of free-moving camera. Corner points provide more accurate information than other pixels and reduce the computation which is unnecessary. Previous works only use intensity information to locate the corner points, however, the information that former and the last frames provided also can be used. We utilize the information to focus on more valuable area and ignore the invaluable area. The proposed algorithm is based on reinforcement learning, which regards the detection of corner points as a Markov process. In the Markov model, the video to be detected is regarded as environment, the selections of blocks for one corner point are regarded as actions and the performance of detection is regarded as state. Corner points are assigned to be the blocks which are seperated from original whole image. Experimentally, we select a conventional method which uses marching and Random Sample Consensus algorithm to obtain objects as the main framework and utilize our algorithm to improve the result. The comparison between the conventional method and the same one with our algorithm show that our algorithm reduce 70% of the false detection.
机译：角点在移动物体检测中起着重要作用，尤其是在自由移动相机的情况下。角点比其他像素提供更准确的信息，并减少了不必要的计算。先前的作品仅使用强度信息来定位拐角点，但是，也可以使用所提供的先前和最后一帧的信息。我们利用这些信息来专注于更有价值的领域，而忽略了宝贵的领域。所提出的算法基于强化学习，该算法将角点的检测视为马尔可夫过程。在马尔可夫模型中，将要检测的视频视为环境，将对一个角点的块选择视为动作，将检测性能视为状态。将角点指定为与原始整个图像分开的块。在实验上，我们选择了一种传统的方法，该方法使用行进和随机样本共识算法来获取对象作为主要框架，并利用我们的算法来改善结果。常规方法与我们的算法的比较表明，我们的算法减少了70％的错误检测。

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