OVERVIEW OF MACHINE LEARNING METHODS FOR REAL-TIME TRACKING SYSTEMS FOR DYNAMIC OBJECTS
DOI:
https://doi.org/10.54309/IJICT.2024.20.4.010Abstract
Significant progress has been made in the development of video analytics systems and individual image authentication technologies. However, challenges persist in recognizing dynamic images due to the complexity and variability of real-world behavior. Certain scenarios place particular importance on extracting information about the structure and motion of objects within a scene, such as indoor video surveillance in crowded areas, robotic system traffic control, and vehicle movement monitoring.
For object tracking tasks, current research and development focus on addressing the following practical challenges:
Variations in scene illumination or image lighting conditions.
Noise generated by camera systems.
Objects that change shape over time.
Temporary disappearance of objects due to occlusion by other objects.
Simultaneous movement of multiple objects with similar characteristics and intersecting trajectories.
In the field of object recognition, there remains a pressing need for real-time algorithms capable of accurately identifying objects in video frames despite interference or noise. Therefore, the development, refinement, and analysis of algorithms for tracking and identifying objects in video footage continue to be critical issues in the current stage of scientific and technological progress.
The primary objective is to develop, enhance, and study new algorithms for object tracking and recognition in video data, considering distortions and interference. This effort aligns with practical requirements for the effective operation of modern security systems.
Keywords: Open Source Computer Vision, Region of interest, Background subtraction, Optical character recognition.
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