TensorFlow平台下的视频目标跟踪深度学习模型设计  

Deep Learning Model Design of Video Target Tracking Based on TensorFlow Platform

作　　者：刘帆 刘鹏远[1] 李兵[1] 徐彬彬 

机构地区：[1]中国人民解放军军械工程学院四系,河北石家庄050003

出　　处：《激光与光电子学进展》2017年第9期277-285,共9页Laser & Optoelectronics Progress

摘　　要：训练模型复杂且训练集庞大导致深度学习的发展受到严重阻碍。使用Google最新开源的TensorFlow软件平台搭建了用于视频目标跟踪的深度学习模型。介绍了深度学习的原理和TensorFlow的平台特性,提出了使用TensorFlow软件平台设计的深度学习模型框架结构,并使用VOT2015标准数据集中的数据设计了相应的实验。经实验验证,该模型具有较高的计算效率和识别精度,并可便捷地调整网络结构,快速找到最优化模型,很好地完成视频目标识别跟踪任务。Due to the complexity of training model and huge training set of deep learning,the development of deep learning is seriously hindered.We use an open-source platform called TensorFlow developed by Google to build deep learning model for video object recognition and tracking.Some basic theories are introduced including the principles of deep learning and TensorFlow′s properties.The framework of deep learning model developed by TensorFlow is outlined.Experiments are designed based on the standard data in VOT2015.Experimental results show that the model has high computational efficiency and recognition accuracy,and it can adjust network structure easily,find optimal structural model fast and complete video object recognition and tracking task well.

关 键 词：机器视觉 TensorFlow 深度学习 计算机视觉 目标跟踪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]