Abstract
Identifying human behaviors is a challenging research problem due to the complexity and variation of appearances and postures, the variation of camera settings, and view angles. In this paper, we try to address the problem of human behavior identification by introducing a novel motion descriptor based on statistical features. The method first divide the video into N number of temporal segments. Then for each segment, we compute dense optical flow, which provides instantaneous velocity information for all the pixels. We then compute Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32 bins. We then compute statistical features from the obtained HOOF forming a descriptor vector of 192- dimensions. We then train a non-linear multi-class SVM that classify different human behaviors with the accuracy of 72.1%. We evaluate our method by using publicly available human action data set. Experimental results shows that our proposed method out performs state of the art methods.
Abstract (translated)
人的行为识别是一个具有挑战性的研究课题，由于人形和姿势的复杂性和变化，相机设置和视角的变化。本文试图通过引入一种基于统计特征的运动描述符来解决人类行为识别问题。该方法首先将视频划分为n个时间段。然后对每一段计算密集光流，为所有像素提供瞬时速度信息。然后，我们计算由范数加权的光流（HOOF）柱状图，并量化为32个箱。然后，我们从获得的胡佛计算统计特征，形成一个192维的描述向量。然后，我们训练一个非线性多类支持向量机，以72.1%的精度对不同的人类行为进行分类。我们使用公开的人类行为数据集来评估我们的方法。实验结果表明，本文提出的方法具有一定的先进性。
URL
https://arxiv.org/abs/1903.02236
