A Violation Analysis Method of Traffic Targets Based on Video and GIS

LUO Xiaoyue; WANG Yanhui; ZHANG Xingguo

doi:10.13203/j.whugis20200582

Volume 48 Issue 4

Apr. 2023

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2023 > 48(4): 647-655. > DOI: 10.13203/j.whugis20200582

LUO Xiaoyue, WANG Yanhui, ZHANG Xingguo. A Violation Analysis Method of Traffic Targets Based on Video and GIS[J]. Geomatics and Information Science of Wuhan University, 2023, 48(4): 647-655. DOI: 10.13203/j.whugis20200582

Citation:

PDF (28630 KB)

A Violation Analysis Method of Traffic Targets Based on Video and GIS

LUO Xiaoyue^{1, 2,},
WANG Yanhui^{1, 2, ,},
ZHANG Xingguo³

1.
College of Resources Environment and Tourism, Capital Normal University, Beijing 100048, China
2.
Key Laboratory of 3-Dimensional Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China
3.
School of Geography Sciences, Xinyang Normal University, Xinyang 464000, China

More Information

Received Date: October 27, 2020
Available Online: April 16, 2023
Published Date: April 04, 2023

Graphical Abstract

Abstract

Abstract

Objectives Previous research on intelligent video analysis methods were unable to detect the violation of vehicles and pedestrians in the video timely and efficiently.
Methods We propose an improved trajectory model (Tra-Model) from the geospatial view, which takes into account the attributes, spatiotemporal relationship and semantic information of dynamic objects in the geographical scene. In particular, we design a traffic violation behavior analysis method based on the rules of trajectory and geometric constraints. Our proposed method can detect three kinds of traffic violations in real time, including retrograde, violation of prohibited marking instructions and no entry. Taking a university as experimental site, we analyze the three types of violations of targets in different traffic scenarios.
Results The experimental results show that: (1) Compared with the existing dynamic target tracking algorithm, the accuracy is improved by 15.6%, and the extracted trajectory contains rich semantic information such as target type and trajectory sequence. (2) The accuracy of our proposed method for the violations analysis from multiple camera sequences is above 70%, which is better than other methods.
Conclusions The proposed method achieves global comprehensiveness, high precision and diversity of detection types for the analysis of various traffic violations in geographical scenarios.
- video GIS,
- target trajectory,
- trajectory model,
- violation detection,
- violation analysis

FullText(HTML)

References (22)

References

[1]	Zhao Z Q, Zheng P, Xu S T, et al. Object Detection with Deep Learning: A Review[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(11): 3212-3232. doi: 10.1109/TNNLS.2018.2876865
[2]	Giannakeris P, Kaltsa V, Avgerinakis K, et al. Speed Estimation and Abnormality Detection from Surveillance Cameras[C]//IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, USA, 2018.
[3]	Sreenu G, Saleem M A. Intelligent Video Surveillance: A Review Through Deep Learning Techniques for Crowd Analysis[J]. Journal of Big Data, 2019, 6(1): 1-27. doi: 10.1186/s40537-018-0162-3
[4]	Ren X H, Wang D, Laskey M, et al. Learning Traffic Behaviors by Extracting Vehicle Trajectories from Online Video Streams[C]//The 14th International Conference on Automation Science and Engineering (CASE), Munich, Germany, 2018.
[5]	蒋恩源, 王学军. 基于跟踪轨迹的车辆异常行为检测[J]. 吉林大学学报(信息科学版), 2016, 34(1): 98-103. https://www.cnki.com.cn/Article/CJFDTOTAL-CCYD201601016.htm Jiang Enyuan, Wang Xuejun. Vehicle Abnormal Behavior Detection Based on Trajectory Tracking[J]. Journal of Jilin University (Information Science Edition), 2016, 34(1): 98-103. https://www.cnki.com.cn/Article/CJFDTOTAL-CCYD201601016.htm
[6]	赵有婷, 李熙莹, 罗东华. 基于视频车辆轨迹模型的交通事件自动检测方法研究[J]. 中山大学学报(自然科学版), 2011, 50(4): 56-60. https://www.cnki.com.cn/Article/CJFDTOTAL-ZSDZ201104012.htm Zhao Youting, Li Xiying, Luo Donghua. Study on the Methods of Automatic Incident Detection Based on the Video Vehicle Trajectory Model[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2011, 50(4): 56-60. https://www.cnki.com.cn/Article/CJFDTOTAL-ZSDZ201104012.htm
[7]	Li C, Han Z, Ye Q, et al. Visual Abnormal Behavior Detection Based on Trajectory Sparse Reconstruction Analysis[J]. Neurocomputing, 2013, 119: 94-100. doi: 10.1016/j.neucom.2012.03.040
[8]	姚兰, 赵永恒, 施雨晴, 等. 一种基于视频分析的高速公路交通异常事件检测算法[J]. 计算机科学, 2020, 47(8): 208-212. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202008033.htm Yao Lan, Zhao Yongheng, Shi Yuqing, et al. Highway Abnormal Event Detection Algorithm Based on Video Analysis[J]. Computer Science, 2020, 47(8): 208-212. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJA202008033.htm
[9]	Sun W Q, Chen S C, Shi L R, et al. Vehicle Following in Intelligent Multi-vehicle Systems Based on SSD-MobileNet[C]//Chinese Automation Congress (CAC), Hangzhou, China, 2020.
[10]	刘学军, 胡加佩, 王美珍, 等. 视频GIS数据采集与处理[J]. 现代测绘, 2017, 40(1): 1-5. doi: 10.3969/j.issn.1672-4097.2017.01.001 Liu Xuejun, Hu Jiapei, Wang Meizhen, et al. Video Acquisition and Processing for Video GIS[J]. Modern Surveying and Mapping, 2017, 40(1): 1-5. doi: 10.3969/j.issn.1672-4097.2017.01.001
[11]	丰江帆, 宋虎. 利用随机图语法的地理视频运动要素解析[J]. 武汉大学学报(信息科学版), 2014, 39(2): 206-209. doi: 10.13203/j.whugis20120711 Feng Jiangfan, Song Hu. Analytical Method for Mobile Elements in Geo-video Using Random Graph Grammar[J]. Geomatics and Information Science of Wuhan University, 2014, 39(2): 206-209. doi: 10.13203/j.whugis20120711
[12]	解愉嘉, 刘学军. 顾及轨迹地理方向的监控视频浓缩方法[J]. 武汉大学学报(信息科学版), 2017, 42(1): 70-76. doi: 10.13203/j.whugis20160080 Xie Yujia, Liu Xuejun. Surveillance Video Synopsis Considering Trajectory Geographic Direction[J]. Geomatics and Information Science of Wuhan University, 2017, 42(1): 70-76. doi: 10.13203/j.whugis20160080
[13]	孔云峰. 地理视频数据模型设计及网络视频GIS实现[J]. 武汉大学学报(信息科学版), 2010, 35(2): 133-137. http://ch.whu.edu.cn/article/id/837 Kong Yunfeng. Design of GeoVideo Data Model and Implementation of Web-Based Video GIS[J]. Geomatics and Information Science of Wuhan University, 2010, 35(2): 133-137. http://ch.whu.edu.cn/article/id/837
[14]	Katkere A, Moezzi S, Kuramura D Y, et al. Towards Video-Based Immersive Environments[J]. Multimedia Systems, 1997, 5(2): 69-85.
[15]	张兴国, 刘学军, 王思宁, 等. 监控视频与2D地理空间数据互映射[J]. 武汉大学学报(信息科学版), 2015, 40(8): 1130-1136. doi: 10.13203/j.whugis20130817 Zhang Xingguo, Liu Xuejun, Wang Sining, et al. Mutual Mapping Between Surveillance Video and 2D Geospatial Data[J]. Geomatics and Information Science of Wuhan University, 2015, 40(8): 1130-1136. doi: 10.13203/j.whugis20130817
[16]	Song H Q, Liu X J, Lü G N, et al. Video Scene Invariant Crowd Density Estimation Using Geographic Information Systems[J]. China Communications, 2014, 11(11): 80-89.
[17]	陈雷, 张荣国, 胡静, 等. 改进的混合高斯模型及阴影消除方法[J]. 计算机应用, 2013, 33(5): 1394-1397. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201305051.htm Chen Lei, Zhang Rongguo, Hu Jing, et al. Improved Gaussian Mixture Model and Shadow Elimination Method[J]. Journal of Computer Applications, 2013, 33(5): 1394-1397. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJY201305051.htm
[18]	朱帅飞, 杨芳, 马伟. 基于ViBe模型和五帧差分法的目标检测算法[J]. 计算机与数字工程, 2020, 48(3): 667-670. https://www.cnki.com.cn/Article/CJFDTOTAL-JSSG202003033.htm Zhu Shuaifei, Yang Fang, Ma Wei. Target Detection Algorithm Based on ViBe Model and Five-frame Difference Method[J]. Computer & Digital Engineering, 2020, 48(3): 667-670. https://www.cnki.com.cn/Article/CJFDTOTAL-JSSG202003033.htm
[19]	Qu H Q, Yuan T Y, Sheng Z Y, et al. A Pedestrian Detection Method Based on YOLOv3 Model and Image Enhanced by Retinex[C]//The 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China, 2017.
[20]	Younis A, Li S X, Jn S, et al. Real-Time Object Detection Using Pre-trained Deep Learning Models MobileNet-SSD[C]//The 6th International Conference on Computing and Data Engineering, Sanya, China, 2020.
[21]	徐涛, 马克, 刘才华. 基于深度学习的行人多目标跟踪方法[J]. 吉林大学学报(工学版), 2021, 51(1): 27-38. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202101002.htm Xu Tao, Ma Ke, Liu Caihua. Multi Object Pedestrian Tracking Based on Deep Learning[J]. Journal of Jilin University (Engineering and Technology Edition), 2021, 51(1): 27-38. https://www.cnki.com.cn/Article/CJFDTOTAL-JLGY202101002.htm
[22]	童星, 张激. 基于SSD_MobileNet模型的ROS平台目标检测[J]. 计算机系统应用, 2019, 28(1): 94-99. https://www.cnki.com.cn/Article/CJFDTOTAL-XTYY201901014.htm Tong Xing, Zhang Ji. Object Detection of ROS Platform Based on SSD_MobileNet Model[J]. Computer Systems & Applications, 2019, 28(1): 94-99. https://www.cnki.com.cn/Article/CJFDTOTAL-XTYY201901014.htm

[1]	MENG Yiyue, GUO Chi, LIU Jingnan. Deep Reinforcement Learning Visual Target Navigation Method Based on Attention Mechanism and Reward Shaping[J]. Geomatics and Information Science of Wuhan University, 2024, 49(7): 1100-1108. DOI: 10.13203/j.whugis20230193
[2]	GAO Kuiliang, LIU Bing, YU Xuchu, YU Anzhu, SUN Yifan. Automatic Network Structure Search Method for Hyperspectral Image Classification[J]. Geomatics and Information Science of Wuhan University, 2024, 49(2): 225-235. DOI: 10.13203/j.whugis20210380
[3]	WANG Jie, LIU Jiahang, LING Xinpeng, DUAN Zexian. Deep Learning-Based Joint Local and Non-local InSAR Image Phase Filtering Method[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20240052
[4]	GUO Congzhou, LI Ke, LI He, TONG Xiaochong, WANG Xiwen. Deep Convolution Neural Network Method for Remote Sensing Image Quality Level Classification[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1279-1286. DOI: 10.13203/j.whugis20200292
[5]	LI Yansheng, ZHANG Yongjun. A New Paradigm of Remote Sensing Image Interpretation by Coupling Knowledge Graph and Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8): 1176-1190. DOI: 10.13203/j.whugis20210652
[6]	JI Shunping, LUO Chong, LIU Jin. A Review of Dense Stereo Image Matching Methods Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2021, 46(2): 193-202. DOI: 10.13203/j.whugis20200620
[7]	ZHANG Liqiang, LI Yang, HOU Zhengyang, LI Xingang, GENG Hao, WANG Yuebin, LI Jingwen, ZHU Panpan, MEI Jie, JIANG Yanxiao, LI Shuaipeng, XIN Qi, CUI Ying, LIU Suhong. Deep Learning and Remote Sensing Data Analysis[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12): 1857-1864. DOI: 10.13203/j.whugis20200650
[8]	JU Yuanzhen, XU Qiang, JIN Shichao, LI Weile, DONG Xiujun, GUO Qinghua. Automatic Object Detection of Loess Landslide Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2020, 45(11): 1747-1755. DOI: 10.13203/j.whugis20200132
[9]	PAN Yin, SHAO Zhenfeng, CHENG Tao, HE Wei. Analysis of Urban Waterlogging Influence Based on Deep Learning Model[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 132-138. DOI: 10.13203/j.whugis20170217
[10]	FAN Heng, XU Jun, DENG Yong, XIANG Jinhai. Behavior Recognition of Human Based on Deep Learning[J]. Geomatics and Information Science of Wuhan University, 2016, 41(4): 492-497. DOI: 10.13203/j.whugis20140110

Cited By

Cited by

Periodical cited type(5)

1.	张爱竹，李忍忍，梁树能，孙根云，付航. 联合样本扩充和谱空迭代的高光谱影像分类. 武汉大学学报(信息科学版). 2025(01): 97-109 .
2.	杜培军，张伟，张鹏，林聪，郭山川，胡泽周. 一种联合空谱特征的高光谱影像分类胶囊网络. 测绘学报. 2023(07): 1090-1104 .
3.	高奎亮，刘冰，余岸竹，徐佰祺，胡伟，胡家玮. 高光谱影像少样例分类的无监督元学习方法. 测绘学报. 2023(11): 1941-1952 .
4.	张彬，刘亮，李晓杰，周伟. 基于深度学习的高光谱影像分类方法研究. 红外与毫米波学报. 2023(06): 825-833 .
5.	孙一帆，余旭初，谭熊，刘冰，高奎亮. 面向小样本高光谱影像分类的轻量化关系网络. 武汉大学学报(信息科学版). 2022(08): 1336-1348 .

Other cited types(1)

Get Citation

PDF

XML

Article views PDF downloads Cited by(6)

A Violation Analysis Method of Traffic Targets Based on Video and GIS

Abstract

References

Related Articles

Cited by

Periodical cited type(5)

Other cited types(1)

Catalog

Related

A Violation Analysis Method of Traffic Targets Based on Video and GIS

Abstract

References

Related Articles

Cited by

Periodical cited type(5)

Other cited types(1)

Catalog

Related

Export File

Citation

Format

Content