# Compressive Change Retrieval for Moving Object Detection

Source: [Compressive Change Retrieval for Moving Object Detection](<https://www.sota2.com/research/paper/68fa62a11e209131ce9fa5e0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T05:50:53.386Z

Authors: Tomoya Murase, Kanji Tanaka

Published: 2016-08-06T02:04:25.000Z

## Abstract

Change detection, or anomaly detection, from street-view images acquired by an autonomous robot at multiple different times, is a major problem in robotic mapping and autonomous driving. Formulation as an image comparison task, which operates on a given pair of query and reference images is common to many existing approaches to this problem. Unfortunately, providing relevant reference images is not straightforward. In this paper, we propose a novel formulation for change detection, termed compressive change retrieval, which can operate on a query image and similar reference images retrieved from the web. Compared to previous formulations, there are two sources of difficulty. First, the retrieved reference images may frequently contain non-relevant reference images, because even state-of-the-art place-recognition techniques suffer from retrieval noise. Second, image comparison needs to be conducted in a compressed domain to minimize the storage cost of large collections of street-view images. To address the above issues, we also present a practical change detection algorithm that uses compressed bag-of-words (BoW) image representation as a scalable solution. The results of experiments conducted on a practical change detection task, "moving object detection (MOD)," using the publicly available Malaga dataset validate the effectiveness of the proposed approach.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1608.02051>)

[PDF](<https://arxiv.org/pdf/1608.02051.pdf>)