A Novel Tuning Method for Real-time Multiple-Object Tracking Utilizing Thermal Sensor with Complexity Motion Pattern

• URL: http://arxiv.org/abs/2507.02408v1
• Date: Thu, 03 Jul 2025 08:03:35 GMT
• Title: A Novel Tuning Method for Real-time Multiple-Object Tracking Utilizing Thermal Sensor with Complexity Motion Pattern
• Authors: Duong Nguyen-Ngoc Tran, Long Hoang Pham, Chi Dai Tran, Quoc Pham-Nam Ho, Huy-Hung Nguyen, Jae Wook Jeon,
• Abstract summary: Multi-Object Tracking in thermal images is essential for surveillance systems.<n>The paper introduces a novel tuning method for pedestrian tracking, specifically designed to handle the complex motion patterns in thermal imagery.
• Score: 7.6016974897939535
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Multi-Object Tracking in thermal images is essential for surveillance systems, particularly in challenging environments where RGB cameras struggle due to low visibility or poor lighting conditions. Thermal sensors enhance recognition tasks by capturing infrared signatures, but a major challenge is their low-level feature representation, which makes it difficult to accurately detect and track pedestrians. To address this, the paper introduces a novel tuning method for pedestrian tracking, specifically designed to handle the complex motion patterns in thermal imagery. The proposed framework optimizes two-stages, ensuring that each stage is tuned with the most suitable hyperparameters to maximize tracking performance. By fine-tuning hyperparameters for real-time tracking, the method achieves high accuracy without relying on complex reidentification or motion models. Extensive experiments on PBVS Thermal MOT dataset demonstrate that the approach is highly effective across various thermal camera conditions, making it a robust solution for real-world surveillance applications.

Related papers

• SPACT18: Spiking Human Action Recognition Benchmark Dataset with Complementary RGB and Thermal Modalities [14.157338282165037]
  Spike cameras, bio-inspired vision sensors, asynchronously fire by accumulating light intensities at each pixel, offering exceptional resolution spikes.<n>This work contributes a dataset that will drive research in energy-efficient, ultra-low-power video understanding, specifically for action recognition using spike-based data.
  arXiv  Detail & Related papers  (2025-07-22T01:59:14Z)
• Thermal Detection of People with Mobility Restrictions for Barrier Reduction at Traffic Lights Controlled Intersections [4.27128470319992]
  Rapid advances in deep learning for computer vision have driven the adoption of RGB camera-based adaptive traffic light systems.<n>Use of RGB cameras presents significant challenges, including limited detection performance under adverse weather or low-visibility conditions.<n>We propose a fully automated, thermal detector-based traffic light system that dynamically adjusts signal durations for individuals with walking impairments or mobility burden.
  arXiv  Detail & Related papers  (2025-05-13T13:44:21Z)
• SMTT: Novel Structured Multi-task Tracking with Graph-Regularized Sparse Representation for Robust Thermal Infrared Target Tracking [8.52497147463548]
  Thermal infrared target tracking is crucial in applications such as surveillance, autonomous driving, and military operations.<n>In this paper, we propose a novel tracker, SMTT, which effectively addresses common challenges in thermal infrared imagery.
  arXiv  Detail & Related papers  (2025-04-20T10:56:15Z)
• Multi-modal Multi-platform Person Re-Identification: Benchmark and Method [58.59888754340054]
  MP-ReID is a novel dataset designed specifically for multi-modality and multi-platform ReID.<n>This benchmark compiles data from 1,930 identities across diverse modalities, including RGB, infrared, and thermal imaging.<n>We introduce Uni-Prompt ReID, a framework with specific-designed prompts, tailored for cross-modality and cross-platform scenarios.
  arXiv  Detail & Related papers  (2025-03-21T12:27:49Z)
• Event-Based Tracking Any Point with Motion-Augmented Temporal Consistency [58.719310295870024]
  This paper presents an event-based framework for tracking any point.<n>It tackles the challenges posed by spatial sparsity and motion sensitivity in events.<n>It achieves 150% faster processing with competitive model parameters.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild [85.03973683867797]
  This paper proposes a concise, elegant, and robust pipeline to estimate smooth camera trajectories and obtain dense point clouds for casual videos in the wild. We show that the proposed method achieves state-of-the-art performance in terms of camera pose estimation even in complex dynamic challenge scenes.
  arXiv  Detail & Related papers  (2024-11-20T13:01:16Z)
• EVIT: Event-based Visual-Inertial Tracking in Semi-Dense Maps Using Windowed Nonlinear Optimization [19.915476815328294]
  Event cameras are an interesting visual exteroceptive sensor that reacts to brightness changes rather than integrating absolute image intensities. This paper proposes the addition of inertial signals in order to robustify the estimation. Our evaluation focuses on a diverse set of real world sequences and comprises a comparison of our proposed method against a purely event-based alternative running at different rates.
  arXiv  Detail & Related papers  (2024-08-02T16:24:55Z)
• Coordinate-Aware Thermal Infrared Tracking Via Natural Language Modeling [16.873697155916997]
  NLMTrack is a coordinate-aware thermal infrared tracking model. NLMTrack applies an encoder that unifies feature extraction and feature fusion. Experiments show that NLMTrack achieves state-of-the-art performance on multiple benchmarks.
  arXiv  Detail & Related papers  (2024-07-11T08:06:31Z)
• SpikeMOT: Event-based Multi-Object Tracking with Sparse Motion Features [52.213656737672935]
  SpikeMOT is an event-based multi-object tracker. SpikeMOT uses spiking neural networks to extract sparsetemporal features from event streams associated with objects.
  arXiv  Detail & Related papers  (2023-09-29T05:13:43Z)
• Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a Light-Weight ToF Sensor [58.305341034419136]
  We present the first dense SLAM system with a monocular camera and a light-weight ToF sensor. We propose a multi-modal implicit scene representation that supports rendering both the signals from the RGB camera and light-weight ToF sensor. Experiments demonstrate that our system well exploits the signals of light-weight ToF sensors and achieves competitive results.
  arXiv  Detail & Related papers  (2023-08-28T07:56:13Z)
• Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning [64.92447072894055]
  Infrared (IR) cameras are robust under adverse illumination and lighting conditions. We propose an algorithm meta-learning framework to improve existing UDA methods. We produce a state-of-the-art thermal detector for the KAIST and DSIAC datasets.
  arXiv  Detail & Related papers  (2021-10-07T02:28:18Z)
• Exploring Thermal Images for Object Detection in Underexposure Regions for Autonomous Driving [67.69430435482127]
  Underexposure regions are vital to construct a complete perception of the surroundings for safe autonomous driving. The availability of thermal cameras has provided an essential alternate to explore regions where other optical sensors lack in capturing interpretable signals. This work proposes a domain adaptation framework which employs a style transfer technique for transfer learning from visible spectrum images to thermal images.
  arXiv  Detail & Related papers  (2020-06-01T09:59:09Z)

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.