SWTrack: 複数仮説スライディングウィンドウによる3D多物体追跡
SWTrack: Multiple Hypothesis Sliding Window 3D Multi-Object Tracking
複数フレームをまとめて処理するスライディングウィンドウとグラフ最適化により、曖昧な対応付けを改善しリアルタイムで高精度な3D多物体追跡を実現した。
著者: Sandro Papais, Robert Ren, Steven Waslander
分類: cs.RO
原文アブストラクト
Modern robotic systems are required to operate in dense dynamic environments, requiring highly accurate real-time track identification and estimation. For 3D multi-object tracking, recent approaches process a single measurement frame recursively with greedy association and are prone to errors in ambiguous association decisions. Our method, Sliding Window Tracker (SWTrack), yields more accurate association and state estimation by batch processing many frames of sensor data while being capable of running online in real-time. The most probable track associations are identified by evaluating all possible track hypotheses across the temporal sliding window. A novel graph optimization approach is formulated to solve the multidimensional assignment problem with lifted graph edges introduced to account for missed detections and graph sparsity enforced to retain real-time efficiency. We evaluate our SWTrack implementation$^{2}$ on the NuScenes autonomous driving dataset to demonstrate improved tracking performance.
