Moving target detection plays an important role in computer vision. However, traditional algorithms such as frame difference and optical flow usually suffer from low accuracy or heavy computation. Recent algorithms such as deep learning-based convolutional neural networks have achieved high accuracy and real-time performance, but they usually need to know the classes of targets in advance, which limits the practical applications. Therefore, we proposed a model free moving target detection algorithm. This algorithm extracts the moving area through the difference of image features. Then, the color and location probability map of the moving area will be calculated through maximum a posteriori probability. And the target probability map can be obtained through the dot multiply between the two maps. Finally, the optimal moving target area can be solved by stochastic gradient descent on the target probability map. Results show that the proposed algorithm achieves the highest accuracy compared with state-of-the-art algorithms, without needing to know the classes of targets. Furthermore, as the existing datasets are not suitable for moving target detection, we proposed a method for producing evaluation dataset. Besides, we also proved the proposed algorithm can be used to assist target tracking.

翻译：移动目标检测在计算机视觉中起着重要作用。 然而, 框架差异和光学流等传统算法通常会受到低精度或重度计算的影响。 最近的算法, 如深学习的进化神经网络, 已经实现了高精度和实时性能, 但是它们通常需要事先知道目标的类别, 从而限制实际应用。 因此, 我们提议了一个模型自由移动目标检测算法。 这个算法通过图像特征的差别来提取移动区域。 然后, 移动区域的颜色和位置概率地图将会通过最大的事后概率来计算。 目标概率地图可以通过两个地图之间的点乘法获得。 最后, 最佳移动目标区域可以通过目标概率地图上的随机梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度梯度来解决。 结果显示, 拟议的算法可以达到与最新水平的算法相比的最高精度, 而不需要知道目标的等级。 此外, 由于现有的数据集不适合移动目标检测, 我们提议了一种方法。 此外, 我们还证明拟议的算法可以用来帮助目标跟踪。