A Kalman filter variant that adaptively weights velocity, acceleration, and jerk terms from observed motion variability improves 3D multi-object tracking by about 0.1% on standard benchmarks and 1.2-1.5% in simulated occlusion scenarios.
Sensor- agnostic graph-aware kalman filter for multi-modal multi-object tracking
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Towards Accurate State Estimation: Motion Dynamics Kalman Filter for 3D Multi-Object Tracking
A Kalman filter variant that adaptively weights velocity, acceleration, and jerk terms from observed motion variability improves 3D multi-object tracking by about 0.1% on standard benchmarks and 1.2-1.5% in simulated occlusion scenarios.