Spatial-Temporal Attention Network for Track-Track Association with Biased Data
Haowei Jia, Gan Wang, Huajun Liu · 2024
Track-track association (TTA) in complex environment for multi-sensor fusion is a challenging topic due to the uncertainty of measurements, biased data, mismatch caused by different resolution etc. In this work, we proposed an end-to-end deep learning model, named the spatial-temporal attention network (STAN) for TTA tasks in complex scenarios. Three modules in the backbone of STAN for intra-track and inter-track feature representation are based on self-attention mechanism, e.g., the motion mode encoder (MME) module to encode the motion pattern of single moving targets, the spatial structure extraction (SSE) module for capturing the inter-track spatial interaction relation of an individual sensor, and the spatialtemporal fusion (STF) module for intra-track modeling on temporal dimensions, respectively. A relation reasoning head (RRH) is built for track-track relation reasoning based on the encoded track features. Experimental results on different tasks show that our proposed method achieved superior performance for track-track association compared with previous methods.