Design And Application Of Lightweight Networks For Target Detection And Tracking

In recent years,with the increasing demand for computer vision,target detection and tracking become one of the important research directions,but there is still some room for development.In order to meet the requirements of high accuracy,there are complex network structures in existing target detection networks.And this leads to problems such as large network model parameters,long training time and high requirements on computer performance,which make many research results unable to be applied to mobile devices.Facing the above problems,this paper processes a lightweight networks for target detection and tracking based on Deep-SORT and SSD networks,named FMDeep-SORT,which can achieve an effective balance between speed,accuracy and size of the model.Firstly,referring to the network structures of MobileNetV2/V3 and ShuffleNetV2,one lightweight network MS-Net is designed.And the parameter amount is only 3.86M.And then combining with inverted residual networks to construct a lightweight target detection network New-SSDLite.Experiments prove that the accuracy rate is 77.56%on the PASCAL VOC 2007 dataset.Secondly,the original target detection module(Faster-RCNN)in Deep-SORT is replaced with New-SSDLite,and combined with the Mean Shift algorithm and Kalman Filtering algorithm,and then a lightweight target tracking network FMDeep-SORT is constructed.Experiments show that FMDeep-SORT achieves better results than the current target tracking algorithms when the target is occluded.Finally,the applicaton of FMDeep-SORT networks in supermarket monitoring has great reference value for counting the number of supermarket repurchases.