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학술지 Adaptive Target Tracking With Interacting Heterogeneous Motion Models
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저자
나기인, 최성록, 김종환
발행일
202211
출처
IEEE Transactions on Intelligent Transportation Systems, v.23 no.11, pp.21301-21313
ISSN
1524-9050
출판사
IEEE
DOI
https://dx.doi.org/10.1109/TITS.2022.3191814
협약과제
21HS4200, 실외 무인 경비 로봇을 위한 멀티모달 지능형 정보분석 기술 개발, 신호철
초록
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.
KSP 제안 키워드
Complementary set, Interacting multiple model(IMM), Motion Pattern, Real-world, Synthetic Datasets, motion model, target tracking
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