[1]李嘎,加云岗,王志晓,等.基于YOLO-CARAFE的人员异常行为识别方法[J].计算机技术与发展,2024,34(06):185-191.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0093]
　LI Ga,JIA Yun-gang,WANG Zhi-xiao,et al.Human Abnormal Behavior Recognition Method Based on YOLO-CARAFE[J].,2024,34(06):185-191.[doi:10.20165/j.cnki.ISSN1673-629X.2024.0093]
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基于YOLO-CARAFE的人员异常行为识别方法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
34
期数:
2024年06期
页码:
185-191
栏目:
新型计算应用系统
出版日期:
2024-06-10

文章信息/Info

Title:
Human Abnormal Behavior Recognition Method Based on YOLO-CARAFE
文章编号:
1673-629X(2024)06-0185-07
作者:
李嘎1; 加云岗1; 王志晓2; 张九龙2; 闫文耀3; 高昂4; 薛尧5
1. 西安工程大学 计算机科学学院,陕西 西安 710600;2. 西安理工大学 计算机科学与工程学院,陕西 西安 710048;3. 延安大学西安创新学院,陕西 西安 710100;4. 国家卫星气象中心,北京 100081;5. 西安交通大学,陕西 西安 710049
Author(s):
LI Ga1; JIA Yun-gang1; WANG Zhi-xiao2; ZHANG Jiu-long2; YAN Wen-yao3; GAO Ang4; XUE Yao5
1. School of Computer Science,Xi’an Polytechnic University,Xi’an 710600,China;2. School of Computer Science and Engineering,Xi’an University of Technology,Xi’an 710048,China;3. Xi’an Innovation College,Yan’an University,Xi’an 710100,China;4. National Satellite Meteorological Center,Beijing 100081,China;5. Xi’an Jiaotong University,Xi’an 710049,China
关键词:
YOLOV7; 行为识别; 损失函数; CARAFE; 深度学习
Keywords:
YOLOV7; behavior recognition; loss function; CARAFE; deep learning
分类号:
TP391
DOI:
10.20165/j.cnki.ISSN1673-629X.2024.0093
摘要:
智能监控中,由于存在环境复杂、监控目标多、画质质量差、人员尺寸不同等因素,从而给人体异常行为识别带来很多挑战。 为了提高视频中人员异常行为识别的准确率和识别效率,提出了人员异常行为识别方法 YOLO-CARAFE。 该方法首先利用轻量级上采样算子 CARAFE 代替最近邻插值上采样算子,CARAFE 不仅利用相邻像素进行工作,还会对相邻像素进行加权融合,可以在大感受野中聚合上下文信息,从而提高在复杂场景下人体异常行为识别时神经网络的特征提取和融合能力;其次,利用 Focal-EIOU 损失函数的难易样本学习策略,使得模型更加关注难以分类的目标对象,有效减小预测框与真实框之间的差异,提高人体异常行为识别的准确度,有效解决异常行为样本数据量少的问题。 通过在自建数据集上的实验表明,YOLO-CARAFE 在人体异常行为识别上具有良好的识别效果,提出的 YOLO-CARAFE 算法在 R 不变的情况下 mAP@ 0. 5, P 分别为 96. 9% ,97. 6% ,提高了 1. 9 百分点,7. 4 百分点,能够满足监控视频中人员异常行为识别对于准确度的需求。
Abstract:
In intelligent monitoring,there are many factors such as complex environment,multiple monitoring targets,poor picture quality,and different personnel sizes,which bring many challenges to human abnormal behavior recognition. In order to improve the accuracy and efficiency of human abnormal behavior recognition in video,we propose YOLO-CARAFE for human abnormal behavior recognition. In this method,CARAFE, a lightweight up - sampling operator, is first used to replace the nearest neighbor interpolation up - sampling operator. CARAFE not only works with adjacent pixels, but also performs weighted fusion of adjacent pixels, which can aggregate context information in the large sensing field, thereby improving the feature extraction and fusion capability of neural networks in recognizing human abnormal behaviors in complex scenes. Secondly,using the difficulty sample learning strategy of Focal-EIOU loss function,the model pays more attention to the target objects that are difficult to classify,effectively reduces the difference between the prediction frame and the real frame, improves the accuracy of human abnormal behavior identification, and effectively solves the characteristics of small amount of abnormal behavior sample data. Experiments on self-built data sets show that YOLO-CARAFE has a good recognition effect on human abnormal behavior recognition. When R is unchanged,mAP@ 0. 5 and P of the proposed YOLO-CARAFE algorithm are 96. 9% and 97. 6% respectively,increasing by 1. 9 percentage points and 7. 4 percentage points. It can meet the accuracy and real-time requirements of abnormal behavior identification in surveillance video.

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更新日期/Last Update: 2024-06-10