Track Segment Association via track graph representation learning

Abstract

Traditional Track Segment Association (TSA) methods used track position vectors or other track information to get association results. However, simply extracting the track position information to form track vectors will lead to the loss of irregular structure information. To solve this problem, a Track Graph Representation Association (TGRA) method is proposed. Through node-level local track point embedding and graph-level track graph embedding, representations of track segments can be obtained and irregular structure information can be retained simultaneously. Then, by the constraint of loss function, track segments belonging to the same target become closer, track segments belonging to different targets become farther in representation space. Finally, the nearest neighbour embedding in representation space is picked as associated tracks. Simulation results demonstrate that TGRA has good adaptability and anti-noise ability, and it can outperform other TSA methods in both quality and efficiency. Compared with the best performance, the average true association rate of TGRA can be increased by 2.8% in short interrupt intervals and 1.3% in long interrupt intervals, respectively.