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计算机系统应用英文版:2022,31(5):277-284
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基于改进MDNet的视频目标跟踪算法
(1.山东建筑大学 信息与电气工程学院, 济南 250101;2.山东省智能建筑技术重点实验室, 济南 250101)
Video Object Tracking Algorithm Based on Improved MDNet
(1.School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China;2.Shandong Provincial Key Laboratory of Intelligent Building Technology, Jinan 250101, China)
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Received:July 06, 2021    Revised:August 11, 2021
中文摘要: 目标跟踪算法面对的突出问题之一是正负样本不均衡, 正样本极度相似. 针对跟踪更新过程中正样本不丰富的问题, 本文基于多域卷积神经网络(MDNet)算法, 提出了一种改进MDNet的视频目标跟踪算法, 首先改进原算法中候选框的选取策略, 提出了一种基于候选框置信度与坐标方差阈值判断相结合的模型更新方法, 其次将原算法的交叉熵损失函数改进为效果更好的focal loss损失函数. 实验结果表明在相同实验环境下本文算法相较于MDNet算法在跟踪准确率和成功率上均有明显提高.
Abstract:One of the major problems of the object tracking algorithm is the imbalance of positive and negative samples, and the positive samples are of high similarity. Aiming at the problem of insufficient positive samples in the tracking update process, this study proposes an improved MDNet-based video object tracking algorithm based on the multi-domain convolutional neural network (MDNet) algorithm. First, the strategy of candidate selection is improved in the original algorithm, and a model update method is presented on the basis of the combination of the candidate confidence and the threshold judgment of coordinate variance. Second, the cross-entropy loss function of the original algorithm is altered to a focal loss function with better performance. The experimental results show that the algorithm has a significant improvement in tracking precision and success rate compared with the MDNet algorithm under the same experimental environment.
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基金项目:山东省重点研发计划(2019GSF111054, 2019GGX104095); 山东省重大科技创新工程(2019JZZY010120)
引用文本:
曹建荣,张玉婷,朱亚琴,武欣莹,杨红娟.基于改进MDNet的视频目标跟踪算法.计算机系统应用,2022,31(5):277-284
CAO Jian-Rong,ZHANG Yu-Ting,ZHU Ya-Qin,WU Xin-Ying,YANG Hong-Juan.Video Object Tracking Algorithm Based on Improved MDNet.COMPUTER SYSTEMS APPLICATIONS,2022,31(5):277-284