状態予測ニューラルネットワークを用いたIMMベースの複数物体追跡
IMM-based Multiple Object Tracking using a State Prediction Neural Network
レーダーのドップラー計測を活用したTransformerベースの変位予測モデルをIMMの運動モードとして統合し、物理モデルの安定性を保ちつつ非線形な物体運動を表現して追跡精度を向上させた。
著者: Chan-Bin Lim, Dong-Hee Paek, Seung-Hyun Kong
分類: cs.RO, cs.AI
原文アブストラクト
Object tracking is essential for autonomous vehicles to avoid obstacles and plan routes. Radar maintains detection performance even in adverse weather and can measure relative velocity through the Doppler effect, making it well suited for object tracking. In this paper, we propose a data-driven state PRedictor-based Interacting Multiple Model tracking method (PR-IMM) that improves nonlinear object-motion representation while preserving the stability and interpretability of physics-based motion models. The proposed method employs a transformer-based PRediction model (PR) that incorporates radar Doppler measurements to predict object displacement. The PR model is integrated into the IMM as a mode alongside the CV, CA, and CT motion models, and their prior positions are dynamically combined according to the mode probabilities. Experimental results show that PR-IMM reduces position-estimation error by 57.3% over the IMM and by 16.5% over the PR, while reducing ID switches by 25.3% and improving IDF1 by 9.6% over the IMM.