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A novel temporal-spatial variable scale algorithm for detecting multiple moving objects

机译：一种检测多个运动物体的时空变尺度算法

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摘要

In this paper, a novel temporal-spatial variable scale algorithm (TSVSA) is presented which proposes to solve the problem of detecting multiple moving objects from complex backgrounds. In general, moving objects have multiscale characteristics in both spatial and temporal domains. In the spatial domain, objects differ in size while in temporal domain they differ in moving speed, which means each object has an optimum temporal-spatial detection window. Here we have formalized the detection of moving objects as the problem of searching in the temporal-spatial domain for multiple distinct optimum subspaces where significant evidence for motion exists. Such subspaces that differ in scale determine the positions in the temporal-spatial domain, moving traces, and other such features of moving objects. Then a criterion for a measurement of motion salience is provided, and a fast recursive algorithm called ???octree decomposition of the temporal-spatial domain??? is proposed. The proposed method can detect and track the objects simultaneously, greatly reducing the incidence of false alarms and miss detection rate. Extensive experiments and detailed analysis are provided in this paper, and multiple experimental results have confirmed the validity and effectiveness of the proposed method.
机译：本文提出了一种新颖的时空可变尺度算法（TSVSA），它提出了解决从复杂背景中检测多个运动物体的问题。通常，运动对象在空间和时间域都具有多尺度特征。在空间域中，对象的大小不同，而在时间域中，它们的移动速度也不同，这意味着每个对象都有一个最佳的时空检测窗口。在这里，我们已经将运动物体的检测形式化为在时空域中搜索存在明显运动证据的多个不同最佳子空间的问题。这种比例不同的子空间确定了时空域中的位置，运动轨迹以及运动对象的其他此类特征。然后提供用于测量运动显着性的标准，以及一种称为“时空域的八叉树分解”的快速递归算法。被提议。所提出的方法可以同时检测和跟踪目标，大大减少了误报的发生率和漏检率。本文提供了广泛的实验和详细的分析，并且多次实验结果证实了该方法的有效性和有效性。

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