China Safety Science Journal ›› 2024, Vol. 34 ›› Issue (2): 83-93.doi: 10.16265/j.cnki.issn1003-3033.2024.02.1125
• Safety social science and safety management • Previous Articles Next Articles
ZHAO Rongyong(), WEI Bingyu**(
), ZHU Wenjie, ZHENG Chengyuan, LI Haonan
Received:
2023-08-12
Revised:
2023-11-18
Online:
2024-02-28
Published:
2024-08-28
Contact:
WEI Bingyu
CLC Number:
ZHAO Rongyong, WEI Bingyu, ZHU Wenjie, ZHENG Chengyuan, LI Haonan. Overview of recognition methods of pedestrian abnormal behaviors in public places[J]. China Safety Science Journal, 2024, 34(2): 83-93.
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Tab.2
Comparison of abnormal behavior recognition performances
类别 | 方法 | 年份 | 数据集 | 性能/% |
---|---|---|---|---|
人工设计 的特征 | STVs描述符+聚类[ | 2013 | UCSD Ped1 | ERR-15.00%(f), 29.00%(p) |
轨迹特征+稀疏重构[ | 2013 | CAVIAR[ | Accuracy-90.42% | |
光流特征+轮廓特征+SVM[ | 2014 | UMN | Accuracy-95.83% | |
人体 骨架 | Openpose+ST-GCN[ | 2019 | 自建数据集 | Accuracy-100.00% |
Alphapose+ST-GCN[ | 2022 | 自建数据集 | Accuracy-98.48% | |
Kinect 3D+逻辑回归[ | 2016 | 自建数据集 | — | |
RGB 图像 | 帧重构+ST-AEs[ | 2017 | CUHK, UCSD Ped1,Ped2 | AUC-80.30%, 89.90%, 87.40% |
帧重构+MemAE[ | 2019 | UCSD Ped1, CUHK, SH.Tech | AUC-94.10%, 83.30%, 71.20% | |
帧预测+U-Net[ | 2018 | CUHK, UCSD Ped1,Ped2 | AUC-84.90%, 83.1%, 95.40% | |
帧预测+边际学习[ | 2019 | CUHK, SH.Tech | AUC-92.80%, 76.80% | |
端对端+深度多实例排序[ | 2018 | 自建数据集 | AUC-75.41% | |
端对端+自训练学习[ | 2020 | UCSD Ped1,Ped2, UMN | AUC-71.70%, 83.20%, 97.25% | |
自监督+多任务学习[ | 2021 | CUHK, UCSD Ped2, SH.Tech | AUC-92.80%, 99.8%, 92.80% | |
帧重构+帧预测[ | 2020 | USCD Ped1,Ped2, CUHK, SH.Tech | AUC-82.60%, 96.20%,83,70%, 71.50% | |
可穿戴 传感器 | 惯性传感器+SVM[ | 2022 | 自建数据集 | F1-96.50% |
惯性传感器+CNN[ | 2020 | 自建数据集 | Accuracy -96.40% |
[1] |
doi: 10.1016/j.neucom.2015.11.021 |
[2] |
张萌, 韩豫, 刘泽锋. 深度学习下建筑工人高空安全防护装备检测方法[J]. 中国安全科学学报, 2022, 32(5): 140-146.
doi: 10.16265/j.cnki.issn1003-3033.2022.05.1141 |
doi: 10.16265/j.cnki.issn1003-3033.2022.05.1141 |
|
[3] |
张晓平, 纪佳慧, 王力, 等. 基于视频的人体异常行为识别与检测方法综述[J]. 控制与决策, 2022, 37(1): 14-27.
|
|
|
[4] |
李建更, 谢海征. 基于姿态估计的人体异常行为识别算法[J]. 北京工业大学学报, 2022, 48(7): 710-720.
|
|
|
[5] |
徐涛, 田崇阳, 刘才华. 基于深度学习的人群异常行为检测综述[J]. 计算机科学, 2021, 48(9): 125-134.
doi: 10.11896/jsjkx.201100015 |
doi: 10.11896/jsjkx.201100015 |
|
[6] |
郭毅博, 孟文化, 范一鸣, 等. 基于可穿戴传感器数据的人体行为识别数据特征提取方法[J]. 计算机辅助设计与图形学学报, 2021, 33(8): 1246-1253.
|
|
|
[7] |
张烈平, 匡贞伍, 李昆键, 等. 基于加速度传感器和神经网络的人体活动行为识别[J]. 现代电子技术, 2019, 42(16): 71-74.
|
|
|
[8] |
陈波, 余秋婷, 陈铁明. 基于传感器人体行为识别深度学习模型的研究[J]. 浙江工业大学学报, 2018, 46(4): 375-381.
|
|
|
[9] |
艾达, 王倩, 樊炜鑫, 等. 智能手机传感器的人体行为识别技术[J]. 西安邮电大学学报, 2020, 25(1): 42-48.
|
|
|
[10] |
|
[11] |
|
[12] |
doi: 10.1016/j.eswa.2017.09.029 |
[13] |
doi: 10.1016/j.engappai.2018.08.014 |
[14] |
doi: 10.1109/TPAMI.2013.111 |
[15] |
杜鉴豪, 许力. 基于区域光流特征的异常行为检测[J]. 浙江大学学报:工学版, 2011, 45(7): 1161-1166.
|
|
|
[16] |
|
[17] |
|
[18] |
|
[19] |
|
[20] |
|
[21] |
|
[22] |
|
[23] |
doi: 10.1016/j.neucom.2012.03.040 |
[24] |
马露, 裴伟, 朱永英, 等. 基于深度学习的跌倒行为识别[J]. 计算机科学, 2019, 46(9): 106-112.
|
doi: 10.11896/j.issn.1002-137X.2019.09.014 |
|
[25] |
杨晨晨, 马春梅, 朱金奇. 基于智能手机的跌倒行为识别算法研究[J]. 计算机工程, 2019, 45(2): 178-183.
doi: 10.19678/j.issn.1000-3428.0048883 |
doi: 10.19678/j.issn.1000-3428.0048883 |
|
[26] |
桑海峰, 陈禹, 何大阔. 基于整体特征的人群聚集和奔跑行为检测[J]. 光电子·激光, 2016, 27(1): 52-60.
|
|
|
[27] |
何鹏, 陈跃跃, 扈啸. 基于智能手表加速度传感器的人体行为识别[J]. 电脑与信息技术, 2015, 23(5): 6-8,33.
|
|
|
[28] |
王冰, 康增建, 吕晓军. 铁路客运站视频监控系统中的行人逆行异常事件检测算法研究[J]. 铁路计算机应用, 2012, 21(4): 19-22.
|
|
|
[29] |
柏万胜, 孙鹏, 郎宇博, 等. 复杂动态背景下视频中逆行检测技术[J]. 警察技术, 2023(3): 44-47.
|
[30] |
doi: 10.1109/TCSVT.2013.2280061 |
[31] |
|
[32] |
|
[33] |
|
[34] |
doi: 10.1023/A:1021669406132 |
[35] |
doi: 10.1016/j.neucom.2019.08.059 |
[36] |
|
[37] |
doi: 10.1109/TPAMI.2022.3222784 pmid: 37145952 |
[38] |
doi: 10.1016/j.knosys.2018.05.029 |
[39] |
|
[40] |
|
[41] |
|
[42] |
|
[43] |
|
[44] |
|
[45] |
刘耀, 焦双健. St-Gcn在建筑工人不安全动作识别中的应用[J]. 中国安全科学学报, 2022, 32(4): 30-35.
doi: 10.16265/j.cnki.issn1003-3033.2022.04.005 |
doi: 10.16265/j.cnki.issn1003-3033.2022.04.005 |
|
[46] |
|
[47] |
|
[48] |
|
[49] |
|
[50] |
doi: 10.1109/TIP.2021.3130545 |
[51] |
|
[52] |
|
[53] |
|
[54] |
doi: 10.1038/323533a0 |
[55] |
|
[56] |
doi: 10.1109/TNNLS.2021.3083152 |
[57] |
|
[58] |
|
[59] |
肖进胜, 申梦瑶, 江明俊, 等. 融合包注意力机制的监控视频异常行为检测[J]. 自动化学报, 2022, 48(12): 2951-2959.
|
|
|
[60] |
|
[61] |
doi: 10.1016/j.patrec.2019.11.024 |
[62] |
|
[63] |
doi: 10.1016/j.neucom.2015.11.095 |
[64] |
doi: 10.3390/s18103533 |
[65] |
周晓芳, 吴松洋, 韩玮, 等. 管控区域人员异常行为检测系统与方法研究[J]. 计算机应用与软件, 2022, 39(5): 92-97.
|
|
|
[66] |
doi: 10.3390/s20185373 |
[67] |
|
[68] |
CAVIAR. Context aware vision using image-based active recognition[EB/OL]. (2005-09-30). https://homepages.inf.ed.ac.uk/rbf/CAVIAR/.
|
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