Action recognition via restricted dense trajectories and spatio-temporal co-occurrence feature

Qing-hui LI; Zhi-gao CUI; Ke JIANG

doi:10.3788/OPE.20182601.0230

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Action recognition via restricted dense trajectories and spatio-temporal co-occurrence feature

Information Sciences | 更新时间：2020-08-13

- Action recognition via restricted dense trajectories and spatio-temporal co-occurrence feature
- Optics and Precision Engineering Vol. 26, Issue 1, Pages: 230-237(2018)
- 作者机构：
  
  火箭军工程大学五系, 陕西西安 710025
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182601.0230
  CLC： TP391
- Received：29 August 2017，
  
  Accepted：09 October 2017，
  
  Published：25 January 2018
- 稿件说明：
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Qing-hui LI, Zhi-gao CUI, Ke JIANG. Action recognition via restricted dense trajectories and spatio-temporal co-occurrence feature[J]. Optics and precision engineering, 2018, 26(1): 230-237.
DOI：

Qing-hui LI, Zhi-gao CUI, Ke JIANG. Action recognition via restricted dense trajectories and spatio-temporal co-occurrence feature[J]. Optics and precision engineering, 2018, 26(1): 230-237. DOI： 10.3788/OPE.20182601.0230.

摘要

针对传统密集轨迹方法应用到真实场景后过多无效轨迹耗费存储与计算资源且严重影响有效特征提取的不足，提出一种新的人体行为识别算法。首先，检测视频帧中存在的人体目标并对获得的包含人体的矩形框进行扩展，利用扩展后的矩形框对传统密集采样特征点的范围进行筛选限制；然后，对筛选限制后的特征点在光流场中跟踪一定帧数获取限制密集轨迹，并在以限制密集轨迹为中心的时空体内构建一组包含轨迹的空间位置、时空上下文信息的特征描述子；最后在视觉词袋模型框架下，采用SVM对特征向量进行编码分类。结果显示：在KTH、YouTube和HMDB51 3个行为数据库上的识别准确率分别达到98.1%、89.7%和66.9%。证明本算法对复杂真实场景中的人体行为具有较高的识别能力。

Abstract

To overcome the limitation of improved dense trajectories for using in real environment

a novel human action recognition algorithm using restricted dense trajectories and spatio-temporal co-occurrence descriptors was proposed. Firstly

a human detector was applied to get the rectangular and the traditional dense interest points in the videos were refined via expanded rectangular box

which greatly reduces the number of trajectories while preserves the discriminative power. Then

the restricted dense trajectories were obtained by tracking the refined points using optical flow fields. And a set of new descriptors was built which describe the relative spatial position and the spatio-temporal context of motion trajectories. Finally

a Bag of Visual Words (BoVW) model with support vector machine was used to classify human action. On three action recognition datasets:KTH

YouTube and HMDB51

the recognition accuracy is 98.1%

89.7% and 66.9% respectively. Experimental results show that the proposed algorithm has higher recognition ability for human action in complex real scenes.

关键词

Keywords

references

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