电子测量与仪器学报

刘雨萌,桑海峰.基于关键帧定位的人体异常行为识别[J].电子测量与仪器学报,2024,38(3):104-111

基于关键帧定位的人体异常行为识别

Human abnormal behavior recognition based on keyframes localization

DOI：

基金项目:国家自然科学基金(62173078)、辽宁省自然科学基金(2022-MS-268)项目资助

作者	单位
刘雨萌	沈阳工业大学信息科学与工程学院沈阳110870
桑海峰	沈阳工业大学信息科学与工程学院沈阳110870

Author	Institution
Liu Yumeng	School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China
Sang Haifeng	School of Information Science and Engineering, Shenyang University of Technology, Shenyang 110870, China

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中文摘要:

近年来，基于视频的人体异常行为识别算法取得了一定的研究成果，但由于监控视频中存储的数据量庞大且视频时间跨度较长，在进行长视频或多行人异常动作检测与识别时，现有的识别方法并不适用。为此，提出了一种基于关键帧定位的人体异常行为识别模型，首先，通过基于标准化流和注意力增强时空图卷积的关键帧定位网络学习正常帧的概率分布，筛选和提取出长视频中的异常帧（关键帧）序列，并将其作为后续网络模型的输入。然后，为了更好地捕捉人体姿势的运动特征和异常情况，提出一种融合注意力和增强残差的时空图卷积异常行为识别算法，将关键帧序列输入到该模型网络中以实现对监控视频中的人体异常行为的高效准确识别。使用公开数据集和自建数据集对该方法的有效性进行验证，实验结果表明，在公开数据集ShanghaiTech Campus上人体异常行为识别的TOP-1准确率达到82.86%，TOP-5准确率达到98.10%，该方法可以更好的完成监控视频中的人体异常行为识别。

英文摘要:

In recent years, video based human abnormal behavior recognition algorithms have achieved certain research results. However, due to the large amount of data stored in surveillance videos and the long time span of videos, existing recognition methods are not suitable for detecting and recognizing abnormal actions in long videos or multiple pedestrians. To this end, a human abnormal behavior recognition model based on keyframe localization is proposed. Firstly, a keyframe localization network based on standardized flow and attention enhanced spatial temporal graph convolution is used to learn the probability distribution of normal frames, filter and extract sequences of abnormal frames (keyframes) in long videos, and use them as inputs for subsequent network models. Then, in order to better capture the motion characteristics and abnormal situations of human posture, a spatial temporal graph convolutional abnormal behavior recognition algorithm that integrates attention and enhanced residuals is proposed. The keyframe sequence is input into the model network to achieve efficient and accurate recognition of human abnormal behavior in surveillance videos. Validate the effectiveness of this method using publicly available and self built datasets. The experimental results show that the TOP-1 accuracy of human abnormal behavior recognition on the publicly available dataset ShanghaiTech Campus reaches 82.86%, and the TOP-5 accuracy reaches 98.10%. This method can better complete the recognition of human abnormal behavior in surveillance videos.

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