Improved Object Tracking Algorithm Based on Twin Convolutional Neural Network
This paper designs a new tracking framework model based on deep learning based on Tensorflow.The convolutional network is used to extract the features,and the crossconvolution is used to get the response map.The classification network is used to judge whether the tracked target is correct,and the classification network is used to obtain accurate target positioning.Open-source dataset are used as the main data,and the collected dataset as supplementary data.OpenCV is used to label the data that is unlabeled,and then checked manually,which can reduce the workload and time cost.The data set is used to train a general target tracker to track the general target and evaluate the performance of the algorithm.The data set is used to train a general target tracker,it realizes the tracking of general targets,and evaluates the performance of the algorithm.
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