Static object detection and segmentation in videos based on dual foregrounds difference with noise filtering

TL;DR

This paper introduces a robust method for detecting and segmenting static objects in cluttered videos by combining frame differencing, noise filtering, and morphological analysis, validated on real and synthetic datasets.

Contribution

It proposes a novel static object detection approach that integrates background subtraction, noise filtering, and morphological operations without prior tracking information.

Findings

Effective detection of static objects in cluttered scenes

Robust segmentation with noise reduction techniques

Validated on multiple datasets showing high accuracy

Abstract

This paper presents static object detection and segmentation method in videos from cluttered scenes. Robust static object detection is still challenging task due to presence of moving objects in many surveillance applications. The level of difficulty is extremely influenced by on how you label the object to be identified as static that do not establish the original background but appeared in the video at different time. In this context, background subtraction technique based on the frame difference concept is applied to the identification of static objects. Firstly, we estimate a frame differencing foreground mask image by computing the difference of each frame with respect to a static reference frame. The Mixture of Gaussian MOG method is applied to detect the moving particles and then outcome foreground mask is subtracted from frame differencing foreground mask. Pre-processing techniques, illumination equalization and de-hazing methods are applied to handle low contrast and to reduce the noise from scattered materials in the air e.g. water droplets and dust particles. Finally, a set of mathematical morphological operation and largest connected-component analysis is applied to segment the object and suppress the noise. The proposed method was built for rock breaker station application and effectively validated with real, synthetic and two public data sets. The results demonstrate the proposed approach can robustly detect, segmented the static objects without any prior information of tracking.