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GrabCut-based human segmentation in video sequences.

Antonio Hernández-Vela¹, Miguel Reyes, Víctor Ponce

¹Departamento MAIA, Universitat de Barcelona, Barcelona, Spain. ahernandez@cvc.uab.cat

Sensors (Basel, Switzerland)

|December 4, 2012

Summary

This study introduces an automatic method for human segmentation and pose recovery, combining tracking and spatial-temporal data for robust results.

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Area of Science:

Computer Vision
Image Processing
Biomedical Engineering

Background:

Accurate human segmentation and pose estimation are crucial for various applications.
Existing methods often struggle with dynamic scenes and require manual initialization.
Integrating spatial and temporal information can improve segmentation robustness.

Purpose of the Study:

To develop a fully automatic Spatio-Temporal GrabCut methodology for human segmentation.
To achieve robust face and pose recovery by combining segmentation with advanced modeling techniques.
To evaluate the performance of the proposed method on public and novel datasets.

Main Methods:

Utilized Histogram of Oriented Gradients (HOG)-based detection, face detection, and skin color models for GrabCut initialization.
Incorporated spatial information using Mean Shift clustering.
Ensured temporal coherence through historical Gaussian Mixture Models.
Combined human segmentation with Active Appearance Models and Conditional Random Fields for full face and pose recovery.

Main Results:

Demonstrated robust human segmentation and accurate face and pose recovery.
Achieved reliable performance on public datasets.
Validated the methodology on a new Human Limb dataset, showing its effectiveness in challenging scenarios.

Conclusions:

The presented Spatio-Temporal GrabCut methodology offers a fully automatic and robust solution for human segmentation and pose recovery.
The integration of spatial and temporal coherence significantly enhances segmentation accuracy.
The combined approach effectively recovers both face and pose information, outperforming existing methods in challenging conditions.