Online multi-target intelligent tracking using a deep long-short term memory network  

作　　者：Yongquan ZHANG Zhenyun SHI Hongbing JI Zhenzhen SU 

机构地区：[1]School of Electronic Engineering,Xidian University,Xi’an 710071,China [2]School of Computer Science and Technology,Xidian University,Xi’an 710071,China

出　　处：《Chinese Journal of Aeronautics》2023年第9期313-329,共17页中国航空学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.62276204);Open Foundation of Science and Technology on Electronic Information Control Laboratory,Natural Science Basic Research Program of Shanxi,China(Nos.2022JM-340 and 2023-JC-QN-0710);China Postdoctoral Science Foundation(Nos.2020T130494 and 2018M633470).

摘　　要：Multi-target tracking is facing the difficulties of modeling uncertain motion and observation noise.Traditional tracking algorithms are limited by specific models and priors that may mismatch a real-world scenario.In this paper,considering the model-free purpose,we present an online Multi-Target Intelligent Tracking(MTIT)algorithm based on a Deep Long-Short Term Memory(DLSTM)network for complex tracking requirements,named the MTIT-DLSTM algorithm.Firstly,to distinguish trajectories and concatenate the tracking task in a time sequence,we define a target tuple set that is the labeled Random Finite Set(RFS).Then,prediction and update blocks based on the DLSTM network are constructed to predict and estimate the state of targets,respectively.Further,the prediction block can learn the movement trend from the historical state sequence,while the update block can capture the noise characteristic from the historical measurement sequence.Finally,a data association scheme based on Hungarian algorithm and the heuristic track management strategy are employed to assign measurements to targets and adapt births and deaths.Experimental results manifest that,compared with the existing tracking algorithms,our proposed MTIT-DLSTM algorithm can improve effectively the accuracy and robustness in estimating the state of targets appearing at random positions,and be applied to linear and nonlinear multi-target tracking scenarios.

关 键 词：Data association Deep long-short term memory network Historical sequence Multi-target tracking Target tuple set Track management 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP212.9[自动化与计算机技术—控制科学与工程]