{"title":"A Metric for Multi-Target Continuous-Time Trajectory Evaluation","authors":"Yue Xin, Yan Song, Tiancheng Li","doi":"10.1109/ICCAIS56082.2022.9990087","DOIUrl":null,"url":null,"abstract":"Existing target trackers, as well as the corresponding evaluation metrics, are based on discrete-time point-state estimates. An emerging approach to target tracking is to estimate the continuous-time trajectories that are given by a state function of time and contain more information than discrete-time point estimates, for which a proper metric is still missing. In this study, a fundamental metric called the integral multi-target trajectory assignment (IMTA) distance that is suitable for evaluating the continuous-time curve trajectories is proposed. Based on optimal matching between the estimated and ground-truth trajectories, the localization distance consists of the integral for the time-consistent trajectory parts and the penalty for the trajectory time-inconsistent parts. Furthermore, the cardinality error is also defined to account for the false alarm and mis-detection in the level of a whole trajectory. Theoretical analysis and numerical examples are presented to demonstrate the performance of the proposed metric.","PeriodicalId":273404,"journal":{"name":"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 11th International Conference on Control, Automation and Information Sciences (ICCAIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCAIS56082.2022.9990087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Existing target trackers, as well as the corresponding evaluation metrics, are based on discrete-time point-state estimates. An emerging approach to target tracking is to estimate the continuous-time trajectories that are given by a state function of time and contain more information than discrete-time point estimates, for which a proper metric is still missing. In this study, a fundamental metric called the integral multi-target trajectory assignment (IMTA) distance that is suitable for evaluating the continuous-time curve trajectories is proposed. Based on optimal matching between the estimated and ground-truth trajectories, the localization distance consists of the integral for the time-consistent trajectory parts and the penalty for the trajectory time-inconsistent parts. Furthermore, the cardinality error is also defined to account for the false alarm and mis-detection in the level of a whole trajectory. Theoretical analysis and numerical examples are presented to demonstrate the performance of the proposed metric.