Comparison of Two Methods for Stationary Incident Detection Based on Background Image

TL;DR

This paper compares two background subtraction methods for detecting temporarily stationary objects in video scenes, focusing on detection accuracy, computational efficiency, and robustness to occlusion and lighting changes.

Contribution

It introduces and evaluates two novel background subtraction schemes for stationary object detection, including dual-background approach with different learning rates.

Findings

Dual-background method improves detection accuracy under occlusion.

Both methods operate in real-time with acceptable computational complexity.

The proposed approach is robust to illumination changes and partial occlusion.

Abstract

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.