Pedestrian Detection via Periodic Motion Analysis
Pedestrian Detection via Periodic Motion Analysis
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DOI：
10.1007/s11263-006-8575-4
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发表时间：
2007-02
影响因子：
19.5
通讯作者：
Yang Ran;I. Weiss;Q. Zheng;L. Davis
中科院分区：
文献类型：
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作者：
Yang Ran;I. Weiss;Q. Zheng;L. Davis
我们描述了红外（和彩色）传感器获取的视频中的行人检测算法。提出了两种基于步态的方法。第一种方法采用计算效率高的周期性测量。与其他方法不同，它使用两个级联假设检验步骤来估计周期性运动频率，以过滤掉非循环像素，以便它在径向和横向行走方向上都能很好地工作。周期的提取是有效的和鲁棒的传感器噪声和杂乱的背景。为了整合形状和运动，我们转换成一个二进制序列的循环模式最大主步态角（MPGA）拟合在第二种方法。它不需要校准，并使用锁相环连续估计周期。这两种方法都通过实验结果进行评估，实验结果测量了作为大小、移动方向、帧速率和序列长度的函数的性能。
We describe algorithms for detecting pedestrians in videos acquired by infrared (and color) sensors. Two approaches are proposed based on gait. The first employs computationally efficient periodicity measurements. Unlike other methods, it estimates a periodic motion frequency using two cascading hypothesis testing steps to filter out non-cyclic pixels so that it works well for both radial and lateral walking directions. The extraction of the period is efficient and robust with respect to sensor noise and cluttered background. In order to integrate shape and motion, we convert the cyclic pattern into a binary sequence by Maximal Principal Gait Angle (MPGA) fitting in the second method. It does not require alignment and continuously estimates the period using a Phase-locked Loop. Both methods are evaluated by experimental results that measure performance as a function of size, movement direction, frame rate and sequence length.