Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Bayesian modeling of dynamic scenes for object detection.

Yaser Sheikh1, Mubarak Shah

  • 1School of Computer Science, University of Central Florida, Orlando 32816, USA. yaser@cs.ucf.edu

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 16, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

STROKEVISION-BENCH: A MULTIMODAL VIDEO AND 2D POSE BENCHMARK FOR TRACKING STROKE RECOVERY.

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing·2026
Same author

Leveraging sparse annotations for leukemia diagnosis on the large leukemia dataset.

Medical image analysis·2025
Same author

DiffAct++: Diffusion Action Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Foundation Models Defining a New Era in Vision: A Survey and Outlook.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Rebuttal to "Comments on 'Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features' ".

IEEE transactions on pattern analysis and machine intelligence·2024
Same author

DFT, GC-MS analysis and biological evaluation of <i>Limbarda crithmoides</i> L. Dumort essential oil; an important edible halophyte grown in Pakistan.

Natural product research·2024
Same journal

Relation DETR+: Exploring Explicit Position Relation Prior for Dense Prediction.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

CAFE: Cross-View Adaptive Fusion and Cluster Center Enhancement for Robust Multi-View Clustering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Shape Anchors for Holistic Indoor Scene Understanding.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

This study presents a novel object detection scheme that leverages pixel intensity correlations and temporal persistence for improved accuracy in dynamic backgrounds. The method uses a joint domain-range model and a MAP-MRF framework for robust moving object detection.

Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Accurate moving object detection is crucial for subsequent tasks like tracking and recognition.
  • Existing methods often struggle with dynamic backgrounds and complex scene dependencies.

Purpose of the Study:

  • To introduce an object detection scheme with innovations in background modeling, foreground detection, and decision-making.
  • To enhance detection accuracy in dynamic environments by exploiting pixel correlations and temporal information.

Main Methods:

  • Challenging the independence of pixel intensities by modeling spatial correlations.
  • Employing nonparametric density estimation on a joint domain-range representation.
  • Utilizing temporal persistence as a detection criterion, modeling foreground objects.

Related Experiment Videos

  • Implementing a MAP-MRF decision framework with graph-based optimization (min-cut).

    • Main Results:

    • Sustained high detection accuracy despite dynamic backgrounds.
    • Effective modeling of multimodal spatial uncertainties and domain-range dependencies.
    • Demonstrated robustness on a diverse set of dynamic scenes.

    Conclusions:

    • The proposed object detection scheme offers significant improvements over existing approaches.
    • Exploiting pixel correlations and temporal persistence enhances performance in challenging dynamic scenes.
    • The MAP-MRF framework efficiently integrates spatial context for reliable object detection.