基于改进SSD的电力检修多目标人员追踪方法

Multi-Object Personnel Tracking Method for Electric Power Maintenance Based on Improved SSD

随着计算机人工智能领域的日益飞速发展,对摄像头数量要求日益增加,视频数据量也在迅速增加,视频下的人形轨迹安全监控跟踪,是大规模智能监控系统的重要研究方向.考虑到安全管控现实场景中不同摄像头的光照亮暗程度和每帧图片的人形角度、尺寸等的差异,会影响人形追踪的准确度,为此提出具有快速优势的CSSD网络(Correct Single Shot multi-box Detector,CSSD)和关联分析应用于人形目标追踪.本文基于行人多目标追踪技术基础,提出了一种CSSD网络来进行模型的检测,并使用简单的卡尔曼滤波方法跟踪预测目标的位置状态,预测检测框位置,使用IOU方法和匈牙利算法来解决视频连续前后帧目标匹配问题.经验证,该方法可以有效地提高人形目标准确率,缓解目标之间的部分遮挡和位置突变问题,能最大程度的适应目标的尺寸、远近和角度改变等.

With the rapid development of computer artificial intelligence,the number of cameras is increasing,and the amount of video data is also increasing rapidly.The security monitoring and tracking of humanoid trajectory in video is an important research direction of large-scale intelligent monitoring system.Considering that the difference of illumination and darkness of different cameras in different security control scenarios and the human angle and size of each frame will affect the accuracy of human tracking,Correct Single Shot multibox Detector(CSSD)network with advantage of fastness and associated analysis are proposed for human tracking.Based on the pedestrian multi-object tracking technology,this study proposes a CSSD network for model detection,and uses ordinary Kalman filter to track and predict the position of the target,predicts the position of the detection box,and uses IOU method and Hungarian algorithm to solve the problem of video frame target matching before and after.It has been proved that this method can effectively improve the accuracy of humanoid targets,alleviate the large changes caused by epigenetic mutation or partial occlusion,and adapt to the size,distance,and angle changes of targets to the greatest extent.