TY - JOUR
T1 - Hierarchical aggregation perceptual pipeline for tactical intention recognition
AU - Li, Ying
AU - Wu, Junsheng
AU - Li, Weigang
AU - Dong, Wei
AU - Fang, Aiqing
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
PY - 2024/6
Y1 - 2024/6
N2 - Tactical intention recognition involves the analysis of target information to interpret and accurately determine hostile intentions, which is crucial for auxiliary decision-making. The recognition challenges are posed by hierarchical and long-term dependencies in tactical intention. To this end, we propose a pipeline that enhances intention recognition performance by perceiving and aggregating the hierarchy information within the tactical context, termed Hierarchical Aggregation Perceptual Pipeline (HAGP). Specifically, the HAGP comprises two pipelines: maneuver features perceive (MFP), and intention features aggregate (IFA). The MFP captures the maneuver features, which are sub-intentioned with hierarchical information, and the IFA aggregates long-term dependencies in each intention. Then, combining these representations to facilitate precise tactical intention recognition. Extensive experimental results on the tactical dataset demonstrate the superiority of our pipeline compared with the state-of-the-art methods.
AB - Tactical intention recognition involves the analysis of target information to interpret and accurately determine hostile intentions, which is crucial for auxiliary decision-making. The recognition challenges are posed by hierarchical and long-term dependencies in tactical intention. To this end, we propose a pipeline that enhances intention recognition performance by perceiving and aggregating the hierarchy information within the tactical context, termed Hierarchical Aggregation Perceptual Pipeline (HAGP). Specifically, the HAGP comprises two pipelines: maneuver features perceive (MFP), and intention features aggregate (IFA). The MFP captures the maneuver features, which are sub-intentioned with hierarchical information, and the IFA aggregates long-term dependencies in each intention. Then, combining these representations to facilitate precise tactical intention recognition. Extensive experimental results on the tactical dataset demonstrate the superiority of our pipeline compared with the state-of-the-art methods.
KW - Deep learning
KW - Hierarchy aggregation
KW - Intention recognition
KW - Neural network
UR - http://www.scopus.com/inward/record.url?scp=85180258753&partnerID=8YFLogxK
U2 - 10.1007/s11042-023-17806-4
DO - 10.1007/s11042-023-17806-4
M3 - 文章
AN - SCOPUS:85180258753
SN - 1380-7501
VL - 83
SP - 58245
EP - 58265
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 20
ER -