Pei-Xin Liu, Zhao-Sheng Zhu, Xiao-Feng Ye, Xiao-Feng Li
{"title":"基于视觉长短期记忆网络的条件随机场跟踪模型","authors":"Pei-Xin Liu, Zhao-Sheng Zhu, Xiao-Feng Ye, Xiao-Feng Li","doi":"10.1016/j.jnlest.2020.100031","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":53467,"journal":{"name":"Journal of Electronic Science and Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.jnlest.2020.100031","citationCount":"2","resultStr":"{\"title\":\"Conditional random field tracking model based on a visual long short term memory network\",\"authors\":\"Pei-Xin Liu, Zhao-Sheng Zhu, Xiao-Feng Ye, Xiao-Feng Li\",\"doi\":\"10.1016/j.jnlest.2020.100031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":53467,\"journal\":{\"name\":\"Journal of Electronic Science and Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.jnlest.2020.100031\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Electronic Science and Technology\",\"FirstCategoryId\":\"95\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1674862X20300288\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Electronic Science and Technology","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1674862X20300288","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Engineering","Score":null,"Total":0}
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.
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.