视频行为识别综述

罗会兰; 王婵娟; 卢飞

doi:10.11959/j.issn.1000-436x.2018107

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视频行为识别综述

综述 | 更新时间：2024-06-05

- 视频行为识别综述
- Survey of video behavior recognition
- 通信学报 2018年39卷第6期页码：169-180
- 作者机构：
  
  江西理工大学信息工程学院，江西赣州 341000
- 作者简介：
  
  [ "罗会兰（1974-），女，江西上高人，博士，江西理工大学教授、硕士生导师，主要研究方向为机器学习、模式识别。" ]
  [ "王婵娟（1992-），女，江西鄱阳人，江西理工大学硕士生，主要研究方向为计算机视觉、行为识别。" ]
  [ "卢飞（1994-），男，江西赣州人，江西理工大学硕士生，主要研究方向为图像处理、机器视觉。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61105042);国家自然科学基金资助项目(61462035);江西省自然科学基金资助项目(20171BAB202014)
- DOI：10.11959/j.issn.1000-436x.2018107
  中图分类号： TP391
- 网络首发：2018-06，
  
  纸质出版：2018-06-25
- 稿件说明：
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罗会兰, 王婵娟, 卢飞. 视频行为识别综述[J]. 通信学报, 2018,39(6):169-180.

Huilan LUO, Chanjuan WANG, Fei LU. Survey of video behavior recognition[J]. Journal on Communications, 2018, 39(6): 169-180.
罗会兰, 王婵娟, 卢飞. 视频行为识别综述[J]. 通信学报, 2018,39(6):169-180. DOI： 10.11959/j.issn.1000-436x.2018107.

Huilan LUO, Chanjuan WANG, Fei LU. Survey of video behavior recognition[J]. Journal on Communications, 2018, 39(6): 169-180. DOI： 10.11959/j.issn.1000-436x.2018107.

摘要

目前行为识别发展迅速，许多基于深度网络自动学习特征的行为识别算法被提出。深度学习方法需要大量数据来训练，对电脑存储、运算能力要求较高。在回顾了当下流行的基于深度网络的行为识别方法的基础上，着重综述了基于手动提取特征的传统行为识别方法。传统行为识别方法通常遵循对视频提取特征并进行建模和预测分类的流程，并将识别流程细分为以下几个步骤进行综述：特征采样、特征描述符选取、特征预/后处理、描述符聚类、向量编码。同时，还对评价算法性能的基准数据集进行了归纳总结。

Abstract

Behavior recognition is developing rapidly

and a number of behavior recognition algorithms based on deep network automatic learning features have been proposed.The deep learning method requires a large number of data to train

and requires higher computer storage and computing power.After a brief review of the current popular behavior recognition method based on deep network

it focused on the traditional behavior recognition methods.Traditional behavior recognition methods usually followed the processes of video feature extraction

modeling of features and classification.Following the basic process

the recognition process was overviewed according to the following steps

feature sampling

feature descriptors

feature processing

descriptor aggregation and vector coding.At the same time

the benchmark data set commonly used for evaluating the algorithm performance was also summarized.

关键词

Keywords

references

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