DMTrack: Deformable State-Space Modeling for UAV Multi-Object Tracking with Kalman Fusion and Uncertainty-Aware Association

• URL: http://arxiv.org/abs/2510.17860v1
• Date: Wed, 15 Oct 2025 13:54:25 GMT
• Title: DMTrack: Deformable State-Space Modeling for UAV Multi-Object Tracking with Kalman Fusion and Uncertainty-Aware Association
• Abstract summary: Multi-object tracking (MOT) from unmanned aerial vehicles (UAVs) presents unique challenges due to unpredictable object motion.<n>We propose DMTrack, a deformable motion tracking framework tailored for UAV-based MOT.<n>Our method operates without appearance models and maintains competitive efficiency, highlighting its practicality for robust UAV-based tracking.
• Score: 18.68212724411998
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-object tracking (MOT) from unmanned aerial vehicles (UAVs) presents unique challenges due to unpredictable object motion, frequent occlusions, and limited appearance cues inherent to aerial viewpoints. These issues are further exacerbated by abrupt UAV movements, leading to unreliable trajectory estimation and identity switches. Conventional motion models, such as Kalman filters or static sequence encoders, often fall short in capturing both linear and non-linear dynamics under such conditions. To tackle these limitations, we propose DMTrack, a deformable motion tracking framework tailored for UAV-based MOT. Our DMTrack introduces three key components: DeformMamba, a deformable state-space predictor that dynamically aggregates historical motion states for adaptive trajectory modeling; MotionGate, a lightweight gating module that fuses Kalman and Mamba predictions based on motion context and uncertainty; and an uncertainty-aware association strategy that enhances identity preservation by aligning motion trends with prediction confidence. Extensive experiments on the VisDrone-MOT and UAVDT benchmarks demonstrate that our DMTrack achieves state-of-the-art performance in identity consistency and tracking accuracy, particularly under high-speed and non-linear motion. Importantly, our method operates without appearance models and maintains competitive efficiency, highlighting its practicality for robust UAV-based tracking.

Related papers

• Segment Anything with Motion, Geometry, and Semantic Adaptation for Complex Nonlinear Visual Object Tracking [81.34609950921023]
  Recent vision foundation models, exemplified by SAM 2, learn strong video understanding priors from large-scale pretraining.<n>We propose SAMOSA, a new tracking framework that adapts SAM 2 to complex VOT scenarios by explicitly leveraging motion, geometry, and semantic cues.
  arXiv  Detail & Related papers  (2026-05-21T14:25:28Z)
• Motion Cues from Image-based Point Tracking for LiDAR Scene Flow Estimation [59.420015367586615]
  We introduce TrackCue, a tracking-guided framework for improving dynamic object representation in LiDAR scene flow estimation.<n>In particular, TrackCue repurposes point tracking to obtain dense image-space trajectories anchored to LiDAR points.<n>We show that TrackCue significantly improves the precision and F1 score of dynamic labels, leading to performance gains in self-supervised scene flow estimation.
  arXiv  Detail & Related papers  (2026-05-16T10:28:54Z)
• SAMOFT: Robust Multi-Object Tracking via Region and Flow [42.6206516982406]
  Multi-object tracking (MOT) is a fundamental task in computer vision that requires continuously tracking multiple targets.<n>Most existing approaches rely on instance-level object features for trajectory association.<n>We propose SAMOFT, a robust tracker that leverages pixel-level cues to improve robustness under complex motion scenarios.
  arXiv  Detail & Related papers  (2026-05-10T08:39:54Z)
• Hypergraph-State Collaborative Reasoning for Multi-Object Tracking [63.32950991964095]
  We propose a collaborative reasoning framework that enhances motion estimation through joint inference among multiple correlated objects.<n>By allowing objects with similar motion states to mutually constrain and refine each other, our framework stabilizes noisy trajectories and infers plausible motion continuity even when target is occluded.
  arXiv  Detail & Related papers  (2026-04-14T12:37:39Z)
• SCT-MOT: Enhancing Air-to-Air Multiple UAVs Tracking with Swarm-Coupled Motion and Trajectory Guidance [3.098803750460157]
  Air-to-air tracking of swarm UAVs presents significant challenges due to the complex nonlinear group motion and weak visual cues for small objects.<n>Existing methods have attempted to improve performance by incorporating trajectory prediction, but they model each object independently.<n>We propose SCTMO-T, a tracking framework that integrates Swarm-Coupled motion modeling and Trajectory-guided feature fusion.
  arXiv  Detail & Related papers  (2026-04-08T09:42:28Z)
• A Tri-Modal Dataset and a Baseline System for Tracking Unmanned Aerial Vehicles [74.8162337823142]
  MM-UAV is the first large-scale benchmark for Multi-Modal UAV Tracking.<n>The dataset spans over 30 challenging scenarios, with 1,321 synchronised multi-modal sequences, and more than 2.8 million annotated frames.<n>Accompanying the dataset, we provide a novel multi-modal multi-UAV tracking framework.
  arXiv  Detail & Related papers  (2025-11-23T08:42:17Z)
• PlugTrack: Multi-Perceptive Motion Analysis for Adaptive Fusion in Multi-Object Tracking [3.976291254896486]
  We propose PlugTrack, a framework that adaptively fuses Kalman filter and data-driven motion predictors through multi-perceptive motion understanding.<n> PlugTrack achieves significant performance gains on MOT17/MOT20 and state-of-the-art on DanceTrack without modifying existing motion predictors.
  arXiv  Detail & Related papers  (2025-11-17T08:03:11Z)
• SocialTrack: Multi-Object Tracking in Complex Urban Traffic Scenes Inspired by Social Behavior [17.501890320034693]
  This paper proposes a novel multi-object tracking framework, SocialTrack, to enhance the tracking accuracy and robustness of small targets in complex urban traffic environments.<n>The specialized small-target detector enhances the detection performance by employing a multi-scale feature enhancement mechanism.<n> Extensive experiments on the UAVDT and MOT17 datasets demonstrate that SocialTrack outperforms existing state-of-the-art (SOTA) methods across several key metrics.
  arXiv  Detail & Related papers  (2025-08-18T09:53:32Z)
• Delving into Dynamic Scene Cue-Consistency for Robust 3D Multi-Object Tracking [16.366398265001422]
  3D multi-object tracking is a critical and challenging task in the field of autonomous driving.<n>We introduce the Dynamic Scene Cue-Consistency Tracker (DSC-Track) to implement this principle.
  arXiv  Detail & Related papers  (2025-08-15T08:48:13Z)
• Tracking the Unstable: Appearance-Guided Motion Modeling for Robust Multi-Object Tracking in UAV-Captured Videos [58.156141601478794]
  Multi-object tracking (UAVT) aims to track multiple objects while maintaining consistent identities across frames of a given video.<n>Existing methods typically model motion cues and appearance separately, overlooking their interplay and resulting in suboptimal tracking performance.<n>We propose AMOT, which exploits appearance and motion cues through two key components: an Appearance-Motion Consistency (AMC) matrix and a Motion-aware Track Continuation (MTC) module.
  arXiv  Detail & Related papers  (2025-08-03T12:06:47Z)
• CAMELTrack: Context-Aware Multi-cue ExpLoitation for Online Multi-Object Tracking [68.24998698508344]
  We introduce CAMEL, a novel association module for Context-Aware Multi-Cue ExpLoitation.<n>Unlike end-to-end detection-by-tracking approaches, our method remains lightweight and fast to train while being able to leverage external off-the-shelf models.<n>Our proposed online tracking pipeline, CAMELTrack, achieves state-of-the-art performance on multiple tracking benchmarks.
  arXiv  Detail & Related papers  (2025-05-02T13:26:23Z)
• MambaMOT: State-Space Model as Motion Predictor for Multi-Object Tracking [23.547018300192065]
  This paper explores the possibilities of replacing the Kalman filter with a learning-based motion model.<n>MambaMOT and MambaMOT+, demonstrate advanced performance on challenging MOT datasets such as DanceTrack and SportsMOT.
  arXiv  Detail & Related papers  (2024-03-16T06:26:52Z)
• MotionTrack: Learning Motion Predictor for Multiple Object Tracking [68.68339102749358]
  We introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT.
  arXiv  Detail & Related papers  (2023-06-05T04:24:11Z)
• MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking [56.92165669843006]
  We propose MotionTrack, which learns robust short-term and long-term motions in a unified framework to associate trajectories from a short to long range. For dense crowds, we design a novel Interaction Module to learn interaction-aware motions from short-term trajectories, which can estimate the complex movement of each target. For extreme occlusions, we build a novel Refind Module to learn reliable long-term motions from the target's history trajectory, which can link the interrupted trajectory with its corresponding detection.
  arXiv  Detail & Related papers  (2023-03-18T12:38:33Z)
• Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion Dynamics [74.1720528573331]
  Unsupervised monocular depth and ego-motion estimation has drawn extensive research attention in recent years. We propose DynaDepth, a novel scale-aware framework that integrates information from vision and IMU motion dynamics. We validate the effectiveness of DynaDepth by conducting extensive experiments and simulations on the KITTI and Make3D datasets.
  arXiv  Detail & Related papers  (2022-07-11T07:50:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.