Data Efficient Visual Place Recognition Using Extremely JPEG-Compressed Images

TL;DR

This paper investigates how JPEG compression affects visual place recognition performance and demonstrates that fine-tuning CNNs can mitigate performance loss in highly compressed images.

Contribution

It provides an in-depth analysis of JPEG compression effects on VPR and proposes CNN fine-tuning as a method to improve recognition in compressed images.

Findings

JPEG compression significantly reduces VPR accuracy.

Higher compression levels cause greater performance degradation.

CNN fine-tuning improves VPR robustness to compression artifacts.

Abstract

Visual Place Recognition (VPR) is the ability of a robotic platform to correctly interpret visual stimuli from its on-board cameras in order to determine whether it is currently located in a previously visited place, despite different viewpoint, illumination and appearance changes. JPEG is a widely used image compression standard that is capable of significantly reducing the size of an image at the cost of image clarity. For applications where several robotic platforms are simultaneously deployed, the visual data gathered must be transmitted remotely between each robot. Hence, JPEG compression can be employed to drastically reduce the amount of data transmitted over a communication channel, as working with limited bandwidth for VPR can be proven to be a challenging task. However, the effects of JPEG compression on the performance of current VPR techniques have not been previously studied. For this reason, this paper presents an in-depth study of JPEG compression in VPR related scenarios. We use a selection of well-established VPR techniques on well-established benchmark datasets with various amounts of compression applied. We show that by introducing compression, the VPR performance is drastically reduced, especially in the higher spectrum of compression. Moreover, this paper demonstrates how fine-tuning a CNN can be utilised as an optimisation method for JPEG compressed data to perform more consistently with the image transformations detected in extremely JPEG compressed images.