# An Integrated Image Filter for Enhancing Change Detection Results

## TL;DR

This paper introduces an integrated image filter combining local and spatiotemporal filtering techniques to enhance change detection results in videos, improving accuracy and robustness across challenging scenes.

## Contribution

The proposed filter uniquely leverages spatiotemporal information and combines local and global filtering, offering a versatile enhancement applicable to various change detection methods.

## Key findings

- Improves change detection masks by utilizing adjacent frame information.
- Handles heavily textured and colorful regions effectively.
- Compatible with multiple change detection techniques and suitable for real-time video processing.

## Abstract

Change detection is a fundamental task in computer vision. Despite significant advances have been made, most of the change detection methods fail to work well in challenging scenes due to ubiquitous noise and interferences. Nowadays, post-processing methods (e.g. MRF, and CRF) aiming to enhance the binary change detection results still fall short of the requirements on universality for distinctive scenes, applicability for different types of detection methods, accuracy, and real-time performance. Inspired by the nature of image filtering, which separates noise from pixel observations and recovers the real structure of patches, we consider utilizing image filters to enhance the detection masks. In this paper, we present an integrated filter which comprises a weighted local guided image filter and a weighted spatiotemporal tree filter. The spatiotemporal tree filter leverages the global spatiotemporal information of adjacent video frames and meanwhile the guided filter carries out local window filtering of pixels, for enhancing the coarse change detection masks. The main contributions are three: (i) the proposed filter can make full use of the information of the same object in consecutive frames to improve its current detection mask by computations on a spatiotemporal minimum spanning tree; (ii) the integrated filter possesses both advantages of local filtering and global filtering; it not only has good edge-preserving property but also can handle heavily textured and colorful foreground regions; and (iii) Unlike some popular enhancement methods (MRF, and CRF) that require either a priori background probabilities or a posteriori foreground probabilities for every pixel to improve the coarse detection masks, our method is a versatile enhancement filter that can be applied after many different types of change detection methods, and is particularly suitable for video sequences.

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Source: https://tomesphere.com/paper/1907.01301