[1]周巧瑜,曹 扬,詹瑾瑜,等.基于 Yolo 和 GOTURN 的景区游客翻越行为识别[J].计算机技术与发展,2022,32(01):134-140.[doi:10. 3969 / j. issn. 1673-629X. 2022. 01. 023]
　ZHOU Qiao-yu,CAO Yang,ZHAN Jin-yu,et al.A Fence Climbing Behavior Recognition of Scenic AreaTourist Based on Yolo and GOTURN[J].,2022,32(01):134-140.[doi:10. 3969 / j. issn. 1673-629X. 2022. 01. 023]
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基于 Yolo 和 GOTURN 的景区游客翻越行为识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年01期
页码:
134-140
栏目:
应用前沿与综合
出版日期:
2022-01-10

文章信息/Info

Title:
A Fence Climbing Behavior Recognition of Scenic AreaTourist Based on Yolo and GOTURN
文章编号:
1673-629X(2022)01-0134-07
作者:
周巧瑜1; 2 ; 曹 扬2; 3 ; 詹瑾瑜1; 2 ; 江 维1 ; 李 响2; 3 ; 杨 瑞2; 3
1. 电子科技大学 信息与软件工程学院,四川 成都 610054;
2. 中电科大数据研究院有限公司,贵州 贵阳 550022;
3. 提升政府治理能力大数据应用技术国家工程实验室,贵州 贵阳 550022
Author(s):
ZHOU Qiao-yu1; 2 ; CAO Yang2; 3 ; ZHAN Jin-yu1; 2 ; JIANG Wei1 ; LI Xiang2; 3 ; YANG Rui2; 3
1. School of Information and Software Engineering,University of Electronic Science andTechnology of China,Chengdu 610054,China;?
2. CETC Big Data Research Institute Co. ,Ltd. ,Guiyang 550022,China;
3. Big Data Application on Improving Government Governanc
关键词:
深度学习; 目标检测; 目标跟踪; 翻越行为识别; Yolo; GOTURN
Keywords:
deep learning; object detection; object tracking; fence climbing behavior recognition; Yolo; GOTURN
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 01. 023
摘要:
近年来,随着旅游市场的快速发展,在旅游景区出现的一些违规行为,不仅危害了人身安全,而且也给社会造成了许多负面影响。 由于出现该类行为的频率不高,? ?通过人工观察耗费大量人力资源且效率不高,使用深度学习算法对具体行为进行识别,帮助景区监管人员快速预警违规行为,已成为必然趋势。 针对这一问题,结合目标检测与目标跟踪任务,该文提出了一种基于 Yolo 和 GOTURN 的景区游客翻越行为识别方法。 首先将视频转为视频帧,再经过 Yolo 目标检测和GOTURN 目标跟踪得到人员边界框坐标和视频帧轨迹点集合,再进入轨迹分析得出最终结果标签( 是否为翻越行为) ,形成一个完整的翻越行为识别方法。 实验数据表明,基于 Yolo 和 GOTURN 的景区游客翻越行为识别方法相对于其他方法具有较高的准确率,应用在实际的景区游客翻越行为识别系统中得到了 93. 7% 的准确率。
Abstract:
In recent years, with the rapid development of tourism market, there are some tourism violation behaviors, which not onlyendanger personal safety,but also cause many negative effects on the society. Due to the infrequent occurrence of such behaviors,manualobservation costs a lot of human resources and is inefficient. It has become an inevitable trend to use deep learning algorithms to identifyspecific behaviors and help scenic area supervisors to quickly warn violation behaviors. For this,combining target detection and targettracking tasks,we introduce a fence climbing behavior recognition method for the scenic area tourists based on Yolo and GOTURN.Firstly,the video is converted to video frame,and then the boundary frame coordinates and the video frame track point set are obtained byYolo target detection and GOTURN target tracking. Finally, through trajectory analysis, the final result label ( whether it is a fenceclimbing behavior or not) is obtained to form a fence climbing behavior recognition method. The experiment shows that the proposedfence climbing behavior recognition method has higher accuracy compared with the other methods,and the accuracy of 93. 7% is obtainedin the actual scenic spot tourist jump behavior recognition system.

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更新日期/Last Update: 2022-01-10