Tool for Semiautomatic Labeling of Moving Objects in Video Sequences: TSLAB

Area of Science:

• Computer Vision
• Artificial Intelligence
• Surveillance Technology

Background:

• Accurate labeling of moving objects in surveillance data is crucial for developing and evaluating detection and tracking algorithms.
• Existing methods for data annotation can be time-consuming and labor-intensive, hindering research progress.

Purpose of the Study:

• To introduce a novel, publicly available tool designed for efficient and user-friendly labeling of surveillance video data.
• To enable the creation of detailed annotations, including moving objects, shadows, and occlusions, at both pixel and object levels.

Main Methods:

• Development of an advanced, user-friendly graphical user interface (GUI).
• Implementation of automated and semi-automatic tools to streamline the labeling process.
• Support for labeling at both pixel and object levels for comprehensive data annotation.

Main Results:

• The proposed tool significantly reduces the time required for labeling surveillance data.
• The GUI facilitates easy and quick annotation of moving objects, shadows, and occlusions.
• The generated labels are suitable for assessing the performance of moving object detection and tracking algorithms.

Conclusions:

• The developed tool provides an efficient solution for annotating surveillance data, supporting the advancement of computer vision research.
• Its user-friendly interface and advanced features democratize the creation of high-quality labeled datasets for moving object analysis.