A Review on Cross Weather Traffic Scene Understanding Using Transfer Learning for Intelligent Transport System

Abstract

Intelligent transport systems (ITS) have revolutionized the transportation industry by integrating cutting-edge technologies to enhance road safety, reduce traffic congestion and optimize the transportation network. Scene understanding is a critical component of ITS that enables real-time decision-making by interpreting the environment's contextual information. However, achieving accurate scene understanding requires vast amounts of labeled data, which can be costly and time-consuming. It is quite challenging to Understand traffic scene captured from vehicle mounted cameras. In recent times, the combination of road scene-graph representations and graph learning techniques has demonstrated superior performance compared to cutting-edge deep learning methods across various tasks such as action classification, risk assessment, and collision prediction. It's a grueling problem due to large variations under different illumination conditions. Transfer learning is a promising approach to address this challenge. Transfer learning involves leveraging pre-trained deep learning models on large-scale datasets to develop efficient models for new tasks with limited data. In the context of ITS, transfer learning can enable accurate scene understanding with less data by reusing learned features from other domains.

This paper presents a comprehensive overview of the application of transfer learning for scene understanding in cross domain. It highlights the benefits of transfer learning for ITS and presents various transfer learning techniques used for scene understanding. This survey paper provides systematic review on cross domain outdoor scene understanding and transfer learning approaches from different perspective, presents information on current state of art and significant methods in choosing the right transfer learning model for specific scene understanding applications.