Adaptive Target Tracking With Interacting Heterogeneous Motion Models

作     者：Na, Ki-In Choi, Sunglok Kim, Jong-Hwan 

作者机构：ETRI Intelligent Robot Res Div Daejeon 34129 South Korea Korea Adv Inst Sci & Technol Robot Program Daejeon 34141 South Korea Seoul Natl Univ Sci & Technol SEOULTECH Dept Comp Sci & Engn Seoul 01811 South Korea Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon 34141 South Korea 

出 版 物：《IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS》 (IEEE Trans. Intell. Transp. Syst.)

年 卷 期：2022年第23卷第11期

页      面：21301-21313页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0814[工学-土木工程] 0823[工学-交通运输工程] 

基　　金：ICT Research and Development Program of MSIP/IITP (Development of multimodal sensor-based intelligent systems for outdoor surveillance robots) [2017-0-00306] 

主　　题：Adaptation models Tracking Target tracking Computational modeling Complexity theory Roads Analytical models Target tracking interacting multiple model heterogeneous motion models Bayesian filtering 

摘      要：Multiple motion estimators such as an interacting multiple model (IMM) have been utilized to track target objects such as cars and pedestrians with diverse motion patterns. However, the standard IMM has limitations in combining motion models with different state definitions, so it cannot contain a complementary set of models that accurately work for all motion patterns. In this paper, we propose IMM-based adaptive target tracking with heterogeneous velocity representations and linear/curvilinear motion models. It can integrate four motion models with different state definitions and dimensions to be completely complimentary for all types of motions. We experimentally demonstrate the effectiveness of the proposed method with accuracy for various motion patterns using two types of datasets: synthetic datasets and real datasets. Experimental results show that the proposed method achieves the adaptive target tracking for diverse types of motion and also for various objects such as cars, pedestrians, and drones in the real world.