计算机与数字工程2024，Vol.52Issue(3)：671-676,6.DOI:10.3969/j.issn.1672-9722.2024.03.006
基于孪生卷积神经网络改进的目标跟踪算法
Improved Object Tracking Algorithm Based on Twin Convolutional Neural Network
卜华雨 1杨国平1
作者信息
- 1. 上海工程技术大学机械与汽车工程学院 上海 201620
- 折叠
摘要
Abstract
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.关键词
目标跟踪/相关滤波/卷积神经网络/Tensorflow/OpenCVKey words
object tracking/correlation filtering/convolutional neural network/Tensorflow/OpenCV分类
信息技术与安全科学引用本文复制引用
卜华雨,杨国平..基于孪生卷积神经网络改进的目标跟踪算法[J].计算机与数字工程,2024,52(3):671-676,6.
