基于OpenCV的人流量监测系统

Pedestrian Flow Monitoring System based on OpenCV

针对人员密集场所人流量统计准确度较低,实时性较差的问题,提出采用基于OpenCV的算法实时进行人流量分析。首先,用Tensorflow中的视频分解为图像算法将采集到的视频分解为帧,对采集到的视频帧图像进行灰度化,去噪声等预处理增强前景物体边缘;其次,通过基于MobileNet V2的SSD算法进行模型训练,meanshift算法进行跟踪检测实现人流量计数;最后,将实时数据通过展示系统输出并实现数据的可视化。结果表明,算法具有较高准确性和实时性。

Aiming at the low accuracy and poor real-time performance of pedestrian flow statistics in densely populated places,this paper proposes an algorithm based on OpenCV for real-time traffic analysis.Firstly,the video is decomposed into frames by using Tensorflow's video decomposition algorithm,and the collected video frame images are grayed and denoised to enhance the edge of the foreground object;secondly,the foreground object edge is enhanced by using MobileNet The SSD algorithm of V2 is used to train the model,and the meanshift algorithm is used to track and detect the pedestrian flow.Finally,the real-time data is output through the display system and the data visualization is realized.Experimental results show that the algorithm has high accuracy and real-time performance.