Adaptive real-time video-tracking for arbitrary objects

In this paper, we present a visual object tracker for mobile systems that is able to specialize to individual objects during tracking. The core of our method is a novel observation model and the way it is automatically adapted to a changing object and background appearance over time. The model is integrated into the well known Condensation algorithm (SIR filter) for statistical inference, and it consists of a boosted ensemble of simple threshold classifiers built upon center-surround Haar-like features, which the filter continuously updates based on the images perceived. We present optimizations and reasonable approximations to limit the computational costs. Thus, the final algorithms are capable of processing video input at real-time. To experimentally investigate the gain of adapting the observation model we compare two different approaches with a non-adapting version of our observation model: maintaining a single observation model for all particles, and maintaining individual observation models for each particle. In addition, experiments were conducted to compare system performances between the proposed algorithms and two other state of the art Condensation based tracking approaches.

Authors

• Dominik Alexander Klein
• Dirk Schulz 0001
• Simone Frintrop
• Armin B. Cremers

Keywords

• Adaptation model
• Computational modeling
• Histograms
• Training
• Target tracking
• Real time systems
• Image color analysis
• Arbitrary Objects
• Tracking Approach
• Training Set
• Binary Classification
• Sequence Typing
• Exponential Function
• Motion Model
• Training Examples
• Particle Filter
• Particles In State
• Real-time Tracking
• Big Change
• Illumination Changes
• Strong Classifier
• Object Appearance
• Classification Confidence
• Feature Pooling
• Object In Frame
• Viewpoint Changes
• Color Histogram
• Camera Pans
• Perspective Transformation
• Adaptive Model
• Target Model
• Part Of Algorithm
• Spatial Model