Abstract
Most existing abandoned object detection algorithms use foreground information generated from background models. Detection using the background subtraction technique performs well under normal circumstances. However, it has a significant problem where the foreground information is gradually absorbed into the background as time passes and disappears, making it very vulnerable to sudden illumination changes that increase the false alarm rate. This paper presents an algorithm for detecting abandoned objects using a dual background model, which is robust even in illumination changes as well as other complex circumstances like occlusion, long-term abandonment, and owner re-attendance. The proposed algorithm can adapt quickly to various illumination changes. And also, it can precisely track the target objects to determine whether it is abandoned regardless of the existence of foreground information and the effect from the illumination changes, thanks to the largest-contour-based presence authentication mechanism proposed in this paper. For performance evaluation, we trialed the algorithm with the PETS2006, ABODA datasets as well as our dataset, especially to demonstrate its robustness in various illumination changes.
Highlights
In the last few years, there has been a growing interest in intelligent video surveillance systems that automatically detect certain events such as intrusion, loitering, abandonment, and fire without the need for constant observation of humans
We will cover experiments to see the limitations of color and texture information using methods such as blurring filters
We presented an algorithm for detecting abandoned objects robustly in illumination changes as well as other complex circumstances like occlusion, long-term abandonment, and owner re-attendance
Summary
In the last few years, there has been a growing interest in intelligent video surveillance systems that automatically detect certain events such as intrusion, loitering, abandonment, and fire without the need for constant observation of humans. The illumination change handling technique introduced above has the advantage of quickly detecting and adapting to the changes, which prevents the algorithm from extracting wrong candidate stationary objects It has a limitation where existing meaningful foreground information generated before the changes can disappear after adaptation. The proposed algorithm only requires the template, which makes it possible for a surveillance system to track an object for long periods, regardless of the problem where the foreground absorbs into the background over time It does not perform any visual analysis for tracking until the pre-defined n seconds have passed since the candidate stationary object was unattended by its owner. This paper mainly focuses on the robust detection of abandoned objects in illumination changes
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