计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 163-169.doi: 10.11896/jsjkx.200800214
管文华, 林春雨, 杨尚蓉, 刘美琴, 赵耀
GUAN Wen-hua, LIN Chun-yu, YANG Shang-rong, LIU Mei-qin, ZHAO Yao
摘要： 近年来,随着智能手机的快速发展,低头族行人在过马路时依然保持浏览手机的姿态,由此造成的交通事故时有发生。如何有效检测低头族成为了当下亟待解决的问题。现有的检测方法需要大量的真实低头异常的数据集,且最终结果存在识别精度不高、速度不尽人意的问题。基于此,提出了一种快速有效的低头异常行人检测方法,与现有方法的区别在于该方法是基于关节点而不是图像。首先设计了一种构造数据集的方法,在识别人体关节点的基础上,调整左右腕关节坐标来模拟行人手持电子设备的姿态,解决了数据集缺少且需要大量标注的问题;其次,提出复杂环境中高效检测行人异常行为的算法,对上述关节点坐标进行分类识别,充分利用手臂与头部信息来实现行人异常行为检测。实验证明,所提算法能够实现实时检测,且检测精度达到了94.08%,从而可以为视频监控、驾驶员、辅助驾驶以及自动驾驶系统提供必要的参考信息。
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| [1]ZHAO Y,LIU D,WANG J X.Study on the impact of pedes-trians crossing streets using mobile phones on traffic safety[J].Journal of Chinese People's Public Security University:Science and Technology,2015,21(2):58-62. [2]ZHOU Y.Research of pedestrian abnormal behavior detectionmethod and implementation of the detection system [D].Nanjing:Southeast University,2016. [3]HU H,JIANG S,LI Z,et al.Abnormal Pedestrian Crossing Detection Based on Video Processing[M]//ICCTP 2011:Towards Sustainable Transportation Systems.2011:1391-1400. [4]WU X,OU Y,QIAN H,et al.A detection system for human abnormal behavior[C]//2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.IEEE,2005:1204-1208. [5]LI Y,XU C,LIU J,et al.Detecting irregularity in videos using kernel estimation and kd trees[C]//Proceedings of the 14th ACM international conference on Multimedia.2006:639-642. [6]SULTANI W,CHEN C,SHAH M.Real-world anomaly detection in surveillance videos[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:6479-6488. [7]PARK S,PARK J,AL-MASNI M,et al.A depth camera-based human activity recognition via deep learning recurrent neural network for health and social care services[J].Procedia Computer Science,2016,100(100):78-84. [8]LI J J,LIU C L,ZHU M.Fast Abnormal Pedestrians Detection Based on Multi-Task CNN in Surveillance Video[J].Computer Systems & Applications,2018,27(11):78-83. [9]DONG Y R.Unconscious behavior detection for pedestrian safety [D].Beijing:Beijing Jiaotong University,2019. [10]WEI S E,RAMAKRISHNA V,KANADE T,et al.Convolu-tional pose machines[C]//Proceedings of the IEEE conference on Computer Vision and Pattern Recognition.2016:4724-4732. [11]NEWELL A,YANG K,DENG J.Stacked hourglass networksfor human pose estimation[C]//European Conference on Computer Vision.Springer,Cham,2016:483-499. [12]FANG H S,XIE S,TAI Y W,et al.Rmpe:Regional multiperson pose estimation[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:2334-2343. [13]CAO Z,SIMON T,WEI S E,et al.Realtime multi-person 2dpose estimation using part affinity fields[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:7291-7299. [14]NEWELL A,HUANG Z,DENG J.Associative embedding:End-to-end learning for joint detection and grouping[C]//Advances in Neural Information Processing Systems.2017:2277-2287. [15]LIU Z,LUO P,QIU S,et al.Deepfashion:Powering robust clothes recognition and retrieval with rich annotations[C]//Pro-ceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:1096-1104. [16]DALAL N,TRIGGS B.Histograms of oriented gradients forhuman detection[C]//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).IEEE,2005,1:886-893. [17]ZHANG S,XIE Y,WAN J,et al.WiderPerson:A diverse dataset for dense pedestrian detection in the wild[J].IEEE Transactions on Multimedia,2020,22(2):380-393. [18]DOLLÁR P,WOJEK C,SCHIELE B,et al.Pedestrian detec-tion:A benchmark[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009:304-311. [19]CHEN X,MA H,WAN J,et al.Multi-view 3d object detection network for autonomous driving[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:1907-1915. |
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