Aims and scope
The coastal and marine environment represents a vital part of the world, resulting in a complex ecosystem tightly linked to many human activities. For this reason, monitoring coastal and marine ecosystems is of critical importance for gaining a better understanding of their complexity with the goal of protecting such a fundamental resource. Coastal and marine environmental monitoring aims to employ leading technologies and methodologies to monitor and evaluate the marine environment both near the coast and underwater. This monitoring can be performed either on site, using sensors for collecting data, or remotely through seafloor cabled observatories, AUVs or ROVs, resulting in a huge amount of data that require advanced intelligent methodologies to extract useful information and knowledge on environmental conditions. A large part of this data is represented by images and videos produced by fixed and PTZ cameras either on the coast, on the marine surface or underwater. For this reason, the analysis of such volume of imagery data imposes a series of unique challenges, which need to be tackled by the computer vision community. The aim of the special session is to host recent research advances in the field of computer vision and image processing techniques applied to the monitoring of coastal and marine environment and to highlight research issues and still open questions.
Relevant topics within this context include, but are not limited to:
• underwater image analysis
• underwater scene understanding
• classification, detection and segmentation of litter
• classification, detection and monitoring of marine life
• autonomous marine navigation
• object tracking
• automatic video annotation and summarization
• context-aware machine learning and image understanding
Paper Submission Guidelines
Papers should be submitted through Easychair (https://easychair.org/conferences/?conf=cvcmem22). See conference web site (https://www.iciap2021.org) for detailed formatting instructions (Springer LaTeX2e Proceedings Template).