Background Subtraction using Compressed Low-resolution Images
This work addresses efficiency improvements for object recognition and tracking in security surveillance and AI applications, but it appears incremental as it builds on existing methods like ViBe and GMM.
The paper tackles the real-time requirement challenge in background subtraction by proposing a novel approach using compressed, low-resolution grayscale images, which preserves salient information effectively as verified by experiments with ViBe and GMM methods.
Image processing and recognition are an important part of the modern society, with applications in fields such as advanced artificial intelligence, smart assistants, and security surveillance. The essential first step involved in almost all the visual tasks is background subtraction with a static camera. Ensuring that this critical step is performed in the most efficient manner would therefore improve all aspects related to objects recognition and tracking, behavior comprehension, etc.. Although background subtraction method has been applied for many years, its application suffers from real-time requirement. In this letter, we present a novel approach in implementing the background subtraction. The proposed method uses compressed, low-resolution grayscale image for the background subtraction. These low-resolution grayscale images were found to preserve the salient information very well. To verify the feasibility of our methodology, two prevalent methods, ViBe and GMM, are used in the experiment. The results of the proposed methodology confirm the effectiveness of our approach.