Fast and Efficient Scene Categorization for Autonomous Driving using VAEs

TL;DR

This paper introduces a fast, compact, and robust scene categorization method for autonomous driving that uses a variational autoencoder to generate global image descriptors, achieving efficient high-level scene recognition.

Contribution

The paper presents a novel approach using VAEs to create interpretable, global image descriptors for scene categorization, improving speed and robustness over existing methods.

Findings

Descriptors are compact with 128 dimensions.

Method is significantly faster to compute.

Descriptors are robust to seasonal and lighting changes.

Abstract

Scene categorization is a useful precursor task that provides prior knowledge for many advanced computer vision tasks with a broad range of applications in content-based image indexing and retrieval systems. Despite the success of data driven approaches in the field of computer vision such as object detection, semantic segmentation, etc., their application in learning high-level features for scene recognition has not achieved the same level of success. We propose to generate a fast and efficient intermediate interpretable generalized global descriptor that captures coarse features from the image and use a classification head to map the descriptors to 3 scene categories: Rural, Urban and Suburban. We train a Variational Autoencoder in an unsupervised manner and map images to a constrained multi-dimensional latent space and use the latent vectors as compact embeddings that serve as global descriptors for images. The experimental results evidence that the VAE latent vectors capture coarse information from the image, supporting their usage as global descriptors. The proposed global descriptor is very compact with an embedding length of 128, significantly faster to compute, and is robust to seasonal and illuminational changes, while capturing sufficient scene information required for scene categorization.