# SWTrack: Multiple Hypothesis Sliding Window 3D Multi-Object Tracking

arXiv: https://arxiv.org/abs/2402.17892 · HTML: https://mlsanity.com/paper/2402.17892/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2024.

## Built on by (most cited first, 5 of 5)

- [ForeSight: Multi-View Streaming Joint Object Detection and Trajectory Forecasting](https://mlsanity.com/paper/2508.07089/) (2025)
- [IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter](https://mlsanity.com/paper/2502.09672/) (2025)
- [OptiPMB: Enhancing 3D Multi-Object Tracking with Optimized Poisson Multi-Bernoulli Filtering](https://mlsanity.com/paper/2503.12968/) (2025)
- [SToRe3D: Sparse Token Relevance in ViTs for Efficient Multi-View 3D Object Detection](https://mlsanity.com/paper/2605.14110/) (2026)
- [BeyondSight: Object Permanence for End-to-End Autonomous Driving](https://mlsanity.com/paper/2607.09138/) (2026)

## Abstract (arXiv)

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 implementation2 on the NuScenes autonomous driving dataset to demonstrate improved tracking performance.