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.
Abstract (translated)
图像变化检测（ICD）是智能车辆（IV）中的一个基本问题，它利用车载单目视觉系统对特定位置的背景模型检测车辆前方的变化物体。从最近大规模的IV应用来看，在空间/时间效率方面，为每个可能的地点培训特定地点的背景模型是不切实际的。为了解决这些问题，我们引入了一种基于自动编码器（ae）的高效ICD框架，它结合了基于ae的异常检测（ad）和基于ae的图像压缩（ic）的优点。我们提出了一种方法，将声发射重建误差作为单一的统一度量，用于训练一组最小的特定位置的声发射，并保持检测精度。我们引入了一个高效的增量递归AE（RAE）训练框架，该框架递归地将大量背景图像汇总到AE集合中。对跨季节ICD任务挑战的实验结果验证了该方法的有效性。
URL
https://arxiv.org/abs/1904.03555
