| 顾及小目标特征的视频人流量智能统计方法 |
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| 引用本文： | 朱军,张天奕,谢亚坤,张杰,李闯农,赵犁,李维炼.顾及小目标特征的视频人流量智能统计方法[J].西南交通大学学报,2022,57(4):705-712, 736. |
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| 作者姓名： | 朱军 张天奕 谢亚坤 张杰 李闯农 赵犁 李维炼 |
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| 作者单位： | 1.西南交通大学地球科学与环境工程学院，四川 成都 6117562.四川豪格远景市政建设有限公司，四川 成都 610036 |
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| 基金项目： | 国家自然科学基金（U2034202,41871289）；;四川省科技计划（2020JDTD0003）； |
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| 摘 要： | 人流量统计对智能安防等领域具有重要研究价值. 针对视频监控系统中人流量统计准确率较低的问题，提出一种顾及小目标特征的视频人流量智能统计方法，重点研究用于小目标检测的Faster R-CNN （Faster region-convolutional neural network）改进算法、运动目标关联匹配以及双向人流量智能统计等关键技术，根据人头目标的小尺度特点对Faster R-CNN网络结构进行适应性改进，利用图像浅层特征提高网络对于小目标的特征提取能力，通过基于轨迹预测的跟踪算法实现运动目标的实时追踪，同时设计双向人流量智能统计算法，以实现人流量高准确率统计；为证明方法的有效性，在密集程度不同的场景中进行了测试. 测试结果表明:改进的目标检测算法相较于原始算法，在Brainwash测试集和Pets2009基准数据集上的平均准确率分别提高了7.31%、10.71%，视频人流量智能统计方法在多种场景下的综合评价指标F值均能达到90.00%以上，相较于SSD-Sort算法和Yolov3-DeepSort算法，其F值提高了1.14% ~ 3.04%.
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| 关 键 词： | 监控视频 人流量统计 Faster R-CNN改进算法 目标检测 目标跟踪 |
| 收稿时间： | 2020-07-06 |
Intelligent Statistic Method for Video Pedestrian Flow Considering Small Object Features |
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| ZHU Jun,ZHANG Tianyi,XIE Yakun,ZHANG Jie,LI Chuangnong,ZHAO Li,LI Weilian.Intelligent Statistic Method for Video Pedestrian Flow Considering Small Object Features[J].Journal of Southwest Jiaotong University,2022,57(4):705-712, 736. |
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| Authors: | ZHU Jun ZHANG Tianyi XIE Yakun ZHANG Jie LI Chuangnong ZHAO Li LI Weilian |
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| Affiliation: | 1.Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China2.Sichuan Haoge Yuanjing Municipal Construction Co., Ltd., Chengdu 610036, China |
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| Abstract: | Pedestrian flow statistics have an important value of research in the fields like intelligent security. In view of the low accuracy in pedestrian flow statistics of video surveillance systems, an intelligent statistic method of video pedestrian flow is proposed with small object features considered. Key technologies are focused in this work such as the improved Faster R-CNN (Faster region-convolutional neural network) algorithm for small object detection, moving object association and matching, and intelligent statistics of bidirectional pedestrian flow. More efforts include adapting the Faster R-CNN network structure according to the small-scale characteristics of the head object, improving the feature extraction ability of the network for small objects by using shallow features of images, and realizing real-time tracking of moving objects through the tracking algorithm based on trajectory prediction. Meanwhile, an intelligent statistic algorithm for bidirectional pedestrian flow is developed to achieve accurate statistics of pedestrian flow. To prove the effectiveness of the proposed method, the experiments were conducted in scenes with different levels of density. The results show that compared with the original algorithm, the improved object detection algorithm improves the mean average precision by 7.31% and 10.71% on the Brainwash test set and Pets2009 benchmark data set, respectively. For the intelligent statistic method of video pedestrian flow, the comprehensive evaluation index F value in various scenes can reach above 90.00%, which is 1.14%?3.04% higher than the excellent methods in recent years. |
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