Automatic Refinement Strategies for Manual Initialization of Object Trackers.

Area of Science:

• Computer Vision
• Human-Computer Interaction
• Machine Learning

Background:

• Object tracking across video frames is crucial for real-time applications.
• Existing algorithms often require accurate manual initialization, which is impractical for live streaming.
• Human input introduces uncertainty and errors into the initialization process.

Purpose of the Study:

• To analyze human initialization errors in object tracking.
• To develop and evaluate a novel refinement strategy to compensate for these errors.
• To improve the robustness of object tracking systems reliant on human input.

Main Methods:

• Collected a dataset of over 20,000 human initialization clicks across three user interface scenarios.
• Analyzed factors influencing human input deviations, developing statistical models.
• Proposed a refinement strategy using objectness cues (color, motion) and visual saliency to generate a likelihood map for accurate window fitting.

Main Results:

• Human initialization consistently contains deviations, particularly with increased object-camera motion.
• The proposed refinement strategy effectively reduces human input errors.
• The method accurately refits object windows based on a single click and derived likelihood map.

Conclusions:

• Human initialization in object tracking is inherently uncertain and error-prone.
• The developed refinement strategy significantly enhances tracking accuracy by mitigating initialization errors.
• This work offers a practical solution for real-time object tracking systems requiring human interaction.