arXiv Machine Learning
SSP-GNN: Learning to Track via Bilevel Optimization
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arXiv Computer Vision
Sep 14
Revisiting Multi-Object Tracking Baselines: Hyperparameter Optimization with Multi-Fidelity Greedy Coordinate Search
arXiv:2609.12261v1 Announce Type: new Abstract: Multi-object tracking (MOT) is dominated by the tracking-by-detection paradigm, whose methods typically rely on a small set of hyperparameters that are...
arXiv Computer Vision
Sep 22
Beyond the Survey: A Systematic Empirical Study of Detection and Association in Visual MOT
This paper conducts a systematic empirical study of multi‑object tracking (MOT) algorithms, focusing on how detection and association components affect overall performance. By evaluating state‑of‑the‑art methods on benchmarks such as MOT16/17/20, SportsMOT, DanceTrack, and CrowdTrack, the authors find that detection quality has a far greater impact than association strategies, and that transformer‑based end‑to‑end models are more robust to detection variations but computationally expensive. The study provides a unified pipeline diagram and practical guidance for researchers and practitioners in selecting and designing MOT systems.
arXiv AI
Jun 17
GOT-JEPA: Generic Object Tracking with Model Adaptation and Occlusion Handling using Joint-Embedding Predictive Architecture
arXiv:2602. 14771v5 Announce Type: replace-cross Abstract: The human visual system tracks objects by integrating current observations with previously observed information, adapting to target and scene changes, and reasoning about occlusion at fine granularity.
arXiv Machine Learning
Jun 19
HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstruction
arXiv:2606. 20437v1 Announce Type: cross Abstract: Charged-particle tracking -- reconstructing trajectories from sparse detector measurements -- is a fundamental high-energy-physics inference problem and a canonical example of learning under extreme combinatorial ambiguity.
arXiv Machine Learning
Jun 5
HDST-GNN: Heterogeneous Dynamic Spatiotemporal Graph Neural Networks for Multi-Object Tracking in UAV Aerial Imagery
arXiv:2606. 05587v1 Announce Type: cross Abstract: Multi-object tracking (MOT) from UAV imagery presents unique challenges: altitude varies across sequences, objects are small and densely packed, and frequent occlusion causes identity switches.
arXiv AI
Jul 3
Rethinking Generic Object Tracking Toward Human-Level Perceptual Intelligence
arXiv:2607. 01395v1 Announce Type: cross Abstract: At the heart of human visual perception lies the ability to maintain a continuous and coherent understanding of the external world.