Context-Aware Sensor Modeling for Asynchronous Multi-Sensor Tracking in Stone Soup

• URL: http://arxiv.org/abs/2603.15137v1
• Date: Mon, 16 Mar 2026 11:34:41 GMT
• Title: Context-Aware Sensor Modeling for Asynchronous Multi-Sensor Tracking in Stone Soup
• Abstract summary: We introduce DetectorContext, an abstraction for the open-source multi-target tracking framework Stone Soup.<n> DetectorContext exposes detection probability and clutter intensity as state-dependent functions evaluated during hypothesis formation.<n>Experiments on asynchronous radar-lidar data demonstrate that context-aware modeling restores stable fusion and significantly improves HOTA and GOSPA performance without increasing false tracks.
• Score: 2.455468619225742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-sensor tracking in the real world involves asynchronous sensors with partial coverage and heterogeneous detection performance. Although probabilistic tracking methods permit detection probability and clutter intensity to depend on state and sensing context, many practical frameworks enforce globally uniform observability assumptions. Under multi-rate and partially overlapping sensing, this simplification causes repeated non-detections from high-rate sensors to erode tracks visible only to low-rate sensors, potentially degrading fusion performance. We introduce DetectorContext, an abstraction for the open-source multi-target tracking framework Stone Soup. DetectorContext exposes detection probability and clutter intensity as state-dependent functions evaluated during hypothesis formation. The abstraction integrates with existing probabilistic trackers without modifying their update equations. Experiments on asynchronous radar-lidar data demonstrate that context-aware modeling restores stable fusion and significantly improves HOTA and GOSPA performance without increasing false tracks.

Related papers

• Zero-shot HOI Detection with MLLM-based Detector-agnostic Interaction Recognition [71.5328300638085]
  Zero-shot Human-object interaction (HOI) detection aims to locate humans and objects in images and recognize their interactions.<n>Existing methods, including two-stage methods, tightly couple interaction recognition with a specific detector.<n>We propose a decoupled framework that separates object detection from IR and leverages multi-modal large language models (MLLMs) for zero-shot IR.
  arXiv  Detail & Related papers  (2026-02-16T19:01:31Z)
• ReSPEC: A Framework for Online Multispectral Sensor Reconfiguration in Dynamic Environments [6.718801307135799]
  We introduce a framework that unifies sensing, learning, and actuation into a closed reconfiguration loop.<n>We implement and evaluate this framework on a mobile rover, showing that adaptive control reduces load by 29.3% with only a 5.3% accuracy drop compared to a baseline.
  arXiv  Detail & Related papers  (2026-02-11T05:39:24Z)
• Diffuse to Detect: A Generalizable Framework for Anomaly Detection with Diffusion Models Applications to UAVs and Beyond [2.4449457537548036]
  Anomaly detection in complex, high-dimensional data, such as UAV sensor readings, is essential for operational safety.<n>We propose the Diffuse to Detect (DTD) framework, a novel approach that adapts diffusion models for anomaly detection.<n>DTD employs a single-step diffusion process to predict noise patterns, enabling rapid and precise identification of anomalies without reconstruction errors.
  arXiv  Detail & Related papers  (2025-10-27T02:08:08Z)
• Belief-Conditioned One-Step Diffusion: Real-Time Trajectory Planning with Just-Enough Sensing [1.6984211127623137]
  We present Belief-Conditioned One-Step Diffusion (B-COD), the first planner that, in a 10 ms forward pass, returns a short-horizon trajectory.<n>We show that this single proxy suffices for a soft-actor-critic to choose sensors online, optimising energy while bounding pose-co growth.
  arXiv  Detail & Related papers  (2025-08-16T21:34:16Z)
• Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection [2.922256022514318]
  We propose an emphAdaptive State-Space Mamba framework for real-time sensor data anomaly detection.<n>Our approach is easily to other time-series tasks that demand rapid and reliable detection capabilities.
  arXiv  Detail & Related papers  (2025-03-26T21:37:48Z)
• DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness [78.98998551326812]
  We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors. We propose a model, termed as DynImp, to handle different time point's missingness with nearest neighbors along feature axis. We show that the method can exploit the multi-modality features from related sensors and also learn from history time-series dynamics to reconstruct the data under extreme missingness.
  arXiv  Detail & Related papers  (2022-09-26T21:59:14Z)
• ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning [51.07496081296863]
  We recouple independent constraints of detection and description of multimodal feature learning with a mutual weighting strategy. We propose a detector that possesses a large receptive field and is equipped with learnable non-maximum suppression layers. We build a benchmark that contains cross visible, infrared, near-infrared and synthetic aperture radar image pairs for evaluating the performance of features in feature matching and image registration tasks.
  arXiv  Detail & Related papers  (2022-05-16T04:24:22Z)
• Tracking the risk of a deployed model and detecting harmful distribution shifts [105.27463615756733]
  In practice, it may make sense to ignore benign shifts, under which the performance of a deployed model does not degrade substantially. We argue that a sensible method for firing off a warning has to both (a) detect harmful shifts while ignoring benign ones, and (b) allow continuous monitoring of model performance without increasing the false alarm rate.
  arXiv  Detail & Related papers  (2021-10-12T17:21:41Z)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• Learning Selective Sensor Fusion for States Estimation [47.76590539558037]
  We propose SelectFusion, an end-to-end selective sensor fusion module. During prediction, the network is able to assess the reliability of the latent features from different sensor modalities. We extensively evaluate all fusion strategies in both public datasets and on progressively degraded datasets.
  arXiv  Detail & Related papers  (2019-12-30T20:25:16Z)

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.