山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (1): 28-34.doi: 10.6040/j.issn.1672-3961.0.2019.179
赵越男1(
),陈桂友1,*(),孙琛1,卢宁1,廖立伟2
Yuenan ZHAO1(),Guiyou CHEN1,*(),Chen SUN1,Ning LU1,Liwei LIAO2
摘要：
针对智能监控领域中存在的目标检测速度慢、安全性分析缺失等问题,提出一种基于空间隐患分布与运动意图解析的危险评估方法。使用k-means++及背景消除法增强YOLO(you only look once)v3算法的目标检测能力,完成人体目标和危险物的识别及定位;利用卡尔曼滤波预测人体目标的移动轨迹,以移动偏向角及人体目标与危险物的距离构建人的运动模式;建立危险度评估模型,依据不同的运动行为模式评估其危险程度。试验结果显示,增强YOLOv3算法在测试集上对各类目标的检测精准度与召回率均超过95%,交并比(intersection over union, IOU)提升7%,帧率达31.3帧/s,满足系统的实时性要求;本研究提出的危险度评估方法能够较好拟合多种运动模式的风险递变规律,危险度评估结果融合了看护目标的运动意图,使得对不同运动模式的安全性评估更加合理。
中图分类号:
| 1 | 黄凯奇, 陈晓棠, 康运锋, 等. 智能视频监控技术综述[J]. 计算机学报, 2015, 38 (6): 1093- 1118. |
| HUANG Kaiqi , CHEN Xiaotang , KANG Yunfeng , et al. Intelligent visual surveillance: a review[J]. Chinese Journal of Computers, 2015, 38 (6): 1093- 1118. | |
| 2 | 尹宏鹏, 陈波, 柴毅, 等. 基于视觉的目标检测与跟踪综述[J]. 自动化学报, 2016, 42 (10): 1466- 1489. |
| YIN Hongpeng , CHEN Bo , CHAI Yi , et al. Vision-based object detection and tracking: a review[J]. Acta Automatica Scinica, 2016, 42 (10): 1466- 1489. | |
| 3 | LI Ce , HAN Zhenjun , YE Qixiang , et al. Visual abnormal behavior detection based on trajectory sparse reconstruction analysis[J]. Neurocomputing, 2013, 119 (16): 94- 100. |
| 4 |
LEE D G , SUK H I , PARK S K , et al. Motion influence map for unusual human activity detection and localization in crowded scenes[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25 (10): 1- 1.
doi: 10.1109/TCSVT.2015.2481139 |
| 5 |
COSAR S , DONATIELLO G , BOGORNY V , et al. Towards abnormal trajectory and event detection in video surveillance[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2017, 27 (3): 683- 695.
doi: 10.1109/TCSVT.2016.2589859 |
| 6 |
XIE Dan , SHU Tianmin , TODOROVIC S , et al. Learning and inferring "Dark Matter" and predicting human intents and trajectories in videos[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40 (7): 1639- 1652.
doi: 10.1109/TPAMI.2017.2728788 |
| 7 |
AHMED S A , DOGRA D P , KAR S , et al. Surveillance scene representation and trajectory abnormality detection using aggregation of multiple concepts[J]. Expert Systems with Applications, 2018, 101, 43- 55.
doi: 10.1016/j.eswa.2018.02.013 |
| 8 | REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE Press, 2016: 779-788. |
| 9 | FELZENSZWALB P, MCALLESTER D, RAMANAN D. A discriminatively trained, multiscale, deformable part model[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, USA: IEEE Press, 2008: 1-8. |
| 10 | GIRSHICK R, DONAHUE J, DARREL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE Press, 2014: 580-587. |
| 11 | GIRSHICK R. Fast R-CNN[C]//Proceedings of IEEE International Conference on Computer Vision. Santiago, Chile: IEEE Press, 2015: 1440-1448. |
| 12 |
REN Shaoqing , HE Kaiming , GIRSHICK R , et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39 (6): 1137- 1149.
doi: 10.1109/TPAMI.2016.2577031 |
| 13 | LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot multiBox detector[C]//Proceedings of European Conference on Computer Vision. Amsterdam, Holland: Springer Cham, 2016: 21-37. |
| 14 | LIN T Y , GOYAL P , GIRSHICK R , et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, (99): 2999- 3007. |
| 15 | REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08) [2018-09-27]. https://arxiv.org/pdf/1804.02767.pdf. |
| 16 | HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE Press, 2016: 770-778. |
| 17 | ZHAO Xia, NI Yingting, JIA Haihang. Modified object detection method based on YOLO[C]//Proceedings of Chinese Conference on Computer Vision. Singapore: Springer, 2017: 233-244. |
| 18 | ARTHUR D, VASSILVITSKII S. K-means++: the advantages of careful seeding[C]//Proceedings of Eighteenth Acm-siam Symposium on Discrete Algorithms. New Orleans, USA: Society for Industrial and Applied Mathematics, 2007: 1027-1035. |
| 19 | REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE Press, 2017: 6517-6525. |
| 20 | CAI Zhaowei, VASCONCELOS N. Cascade R-CNN: delving into high quality object detection[C]//Proceedings of Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE Press, 2018: 6154-6162. |
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