  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.
