# Place-specific Background Modeling Using Recursive Autoencoders

## TL;DR

This paper presents a recursive autoencoder framework for place-specific background modeling in vehicle image change detection, improving efficiency and accuracy in large-scale, cross-season scenarios.

## Contribution

It introduces an incremental recursive autoencoder training method that efficiently summarizes large background image collections for improved change detection.

## Key findings

- Effective in cross-season ICD tasks
- Reduces training complexity and data storage
- Maintains high detection accuracy

## Abstract

Image change detection (ICD) to detect changed objects in front of a vehicle with respect to a place-specific background model using an on-board monocular vision system is a fundamental problem in intelligent vehicle (IV). From the perspective of recent large-scale IV applications, it can be impractical in terms of space/time efficiency to train place-specific background models for every possible place. To address these issues, we introduce a new autoencoder (AE) based efficient ICD framework that combines the advantages of AE-based anomaly detection (AD) and AE-based image compression (IC). We propose a method that uses AE reconstruction errors as a single unified measure for training a minimal set of place-specific AEs and maintains detection accuracy. We introduce an efficient incremental recursive AE (rAE) training framework that recursively summarizes a large collection of background images into the AE set. The results of experiments on challenging cross-season ICD tasks validate the efficacy of the proposed approach.