Compressive Change Retrieval for Moving Object Detection

TL;DR

This paper introduces a novel compressive change retrieval method for moving object detection in street-view images, enabling effective change detection using web-retrieved reference images in a compressed domain.

Contribution

It proposes a new formulation for change detection that operates on compressed images retrieved from the web, addressing retrieval noise and storage issues.

Findings

Effective change detection demonstrated on Malaga dataset

Robustness to retrieval noise in reference images

Scalable solution using compressed bag-of-words representation

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.