Research on Group Behavior Recognition Method Based on Multi-Stream Architecture and Long Short-Term Memory Network

doi:10.3969/j.issn.0372-2112.2020.04.022

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Research on Group Behavior Recognition Method Based on Multi-Stream Architecture and Long Short-Term Memory Network

更新时间：2025-07-08

- Research on Group Behavior Recognition Method Based on Multi-Stream Architecture and Long Short-Term Memory Network
- Acta Electronica Sinica Vol. 48, Issue 4, Pages: 800-807(2020)
- 作者机构：
  
  青岛科技大学信息科学技术学院,山东,青岛,266001
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61672305)
- DOI：10.3969/j.issn.0372-2112.2020.04.022
  CLC： TP301.6
- Published Online：25 April 2020，
  
  Published：2020
- 稿件说明：
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Research on Group Behavior Recognition Method Based on Multi-Stream Architecture and Long Short-Term Memory Network[J]. Acta Electronica Sinica, 2020, 48(4): 800-807.
DOI：

Research on Group Behavior Recognition Method Based on Multi-Stream Architecture and Long Short-Term Memory Network[J]. Acta Electronica Sinica, 2020, 48(4): 800-807. DOI： 10.3969/j.issn.0372-2112.2020.04.022.

摘要

提出一种基于多流架构与长短时记忆网络的上下文建模框架，旨在克服组群行为识别的两个难点，其一对复杂场景中多视觉线索进行信息融合；其二对情景人物进行建模，以获得长视频上下文关系.并且，对基于全局信息和基于局部信息的识别结果进行决策融合，判定最终组群行为属性.该算法在CAD1和CAD2上分别取得93.2%和95.7%平均识别率.

Abstract

This paper proposes a context modeling framework based on multi-stream architecture and LSTM

which aims to overcome two difficulties for group behavior recognition. One is to fuse information from multiple visual cues in complex scenes

the other is to model situational characters to get the long-term temporal context in the video. In addition

decision fusion is performed on the behavior recognition results based on global information and local information to determine the final group behavior attributes. The algorithm achieved 93.2% and 95.7% average recognition rates on CAD1 and CAD2 respectively.

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