  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.
