电子监控部分遮挡目标单模态自监督信息挖掘技术OA北大核心CSTPCD

In allusion to the difficulty of identifying occlusive targets in electronic surveillance video,the single-mode self-supervised information mining technology for partially occlusive targets in electronic monitoring is researched.In order to obtain the state information of the target,the occlusion detection method is used to judge whether there is a partially occluded target in the surveillance video.When there are partially occluded targets in surveillance video,subtraction clustering method is used to identify,track or describe specific targets,and provide more accurate and detailed target feature information.On this basis,the supervised occlusion target feature learning discrimination loss function constructed by cross entropy loss function and soft interval triplet loss function is used as the objective function for partially occluded target information mining.In each batch of electronic monitoring samples,the minimum distance negative sample pair and the maximum distance positive sample pair are mined,and the parameters are optimized by the backpropagation.From this,the sample of electronic monitoring image is input,and the results of self-supervised information mining are obtained by means of the forward propagation.The experimental results show that this technology can effectively mine the partially occluded targets of electronic monitoring,and the mAP value of target mining is higher than 0.9,which can provide a reliable basis for improving the recognition accuracy of monitoring targets.