Comparison of Two Methods for Stationary Incident Detection Based on Background Image
This work addresses stationary object detection in visual tracking for applications like surveillance, but it is incremental as it builds on existing background subtraction methods.
The paper tackled the problem of detecting temporarily stationary objects in video scenes using background subtraction, proposing two schemes (single vs. dual backgrounds) and comparing them in terms of detection performance and computational complexity, with results showing robustness to occlusion and illumination changes and real-time operation.
In general, background subtraction-based methods are used to detect moving objects in visual tracking applications. In this paper, we employed a background subtraction-based scheme to detect the temporarily stationary objects. We proposed two schemes for stationary object detection, and we compare those in terms of detection performance and computational complexity. In the first approach, we used a single background, and in the second approach, we used dual backgrounds, generated with different learning rates, in order to detect temporarily stopped objects. Finally, we used normalized cross correlation (NCC) based image comparison to monitor and track the detected stationary object in a video scene. The proposed method is robust with partial occlusion, short-time fully occlusion, and illumination changes, and it can operate in real time.