Implementation of Video Monitoring and Tracking Technology Based on Improved SECOND Network and MobileNet V2 Network
In order to improve the job site video monitoring level,a video monitoring and tracking method based on deep learning is proposed.This method enables the improvement of the SECOND network by adopting Res2SENet network as the convolution module of SECOND network and using deformable convolution to re-place the standard convolution of SECOND network.Firstly,the target is detected based on the improved SEC-OND network,and the job site video monitoring target detection is realized.Then,the detection and tracking of job site video monitoring target is realized by adopting dilation convolution to replace the ordinary convolution of the MobileNet V2 network and using the improved MobileNet V2 network as the target tracking algorithm.Finally,test is performed on a typical KITTI dataset containing a large number of laser point cloud images.The results show that the average accuracy and detection time of the method utilizing the improved SECOND net-work to detect the job site video monitoring 3D targets are 81.62％and 0.048 s,respectively,which means that the improved SECOND network has obvious advantages over the standard SECOND network,feature pyra-mid network(FPN)network and F-PointNet network.The accuracy,precision and tracking number using the improved MobileNet V2 network to detect the job site video monitoring 3D targets are 81.62％,80.55％and 57.30％,respectively.The number of lost and instantaneous conversion times of pedestrian ID in the tracking trajectory are 11.08％and 22％,respectively.It has a faster operating speed,which is 39 f/s.Therefore,com-pared with MobileNet V2 network,MDP network and SSVM network,the improved MobileNet V2 network has certain advantages in various indicators,and can meet the detection and real-time tracking requirements of the job site video monitoring target.
deep learningvideo monitoringtarget detectiontarget tracking
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