基于视觉长短期记忆网络的条件随机场跟踪模型

Q1 Engineering
Pei-Xin Liu, Zhao-Sheng Zhu, Xiao-Feng Ye, Xiao-Feng Li
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引用次数: 2

摘要

在密集的行人跟踪中,频繁的物体遮挡和物体之间的近距离会给准确估计物体轨迹带来困难。在本研究中,利用三维空间中的视觉长短期记忆网络和对目标轨迹段进行联合运动估计,建立了条件随机场跟踪模型。在长短期记忆网络中加入目标视野信息,提高运动相关目标对选择和运动估计的准确性。针对运动轨迹段长度和间隔的不确定性,提出了一种多模式长短期记忆网络进行目标运动估计。使用PETS2009数据集评估跟踪性能。实验结果表明,该方法比基于独立运动估计的跟踪方法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conditional random field tracking model based on a visual long short term memory network

In dense pedestrian tracking, frequent object occlusions and close distances between objects cause difficulty when accurately estimating object trajectories. In this study, a conditional random field tracking model is established by using a visual long short term memory network in the three dimensional space and the motion estimations jointly performed on object trajectory segments. Object visual field information is added to the long short term memory network to improve the accuracy of the motion related object pair selection and motion estimation. To address the uncertainty of the length and interval of trajectory segments, a multimode long short term memory network is proposed for the object motion estimation. The tracking performance is evaluated using the PETS2009 dataset. The experimental results show that the proposed method achieves better performance than the tracking methods based on independent motion estimation.

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来源期刊
Journal of Electronic Science and Technology
Journal of Electronic Science and Technology Engineering-Electrical and Electronic Engineering
CiteScore
4.30
自引率
0.00%
发文量
1362
审稿时长
99 days
期刊介绍: JEST (International) covers the state-of-the-art achievements in electronic science and technology, including the most highlight areas: ¨ Communication Technology ¨ Computer Science and Information Technology ¨ Information and Network Security ¨ Bioelectronics and Biomedicine ¨ Neural Networks and Intelligent Systems ¨ Electronic Systems and Array Processing ¨ Optoelectronic and Photonic Technologies ¨ Electronic Materials and Devices ¨ Sensing and Measurement ¨ Signal Processing and Image Processing JEST (International) is dedicated to building an open, high-level academic journal supported by researchers, professionals, and academicians. The Journal has been fully indexed by Ei INSPEC and has published, with great honor, the contributions from more than 20 countries and regions in the world.
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