Call for Papers
First International Workshop on
AI-based All-Weather Surveillance System, AWSS 2024
in conjunction with ACCV 2024
Main Organizers
Thierry Bouwmans, Associate Professor (HDR), Laboratoire MIA, La
Rochelle Université, France, Email : tbouwman@univ-lr
Santosh Kumar Vipparthi, Associate Professor, Dept. of Computer
Science & Engineering, MNIT, Jaipur, India, Email :
kvipparthi@iitrpr.ac.in
Subrahmanyam Murala, Trinity College Dublin, Ireland, Email:
muralas@tcd.ie
Sajid Javed, Khalifa University of Science and Technology, UAE, Email
: sajid.javed@ku.ac.ae
Description (AWSS 2024 (google.com))
https://sites.google.com/view/awss2024/accueil
Advances in computer vision and the falling costs of camera hardware
have allowed the massive deployment of cameras for monitoring physical
premises. The extensive deployment of fixed and movable cameras for
control and safety has resulted in visual data collection for online
and post-event analysis. However, different environmental conditions
such as haze or fog, snow, dust, raindrops, and rain streaks degrade
the perceptual quality of the data, eventually affecting the
architecture performance on high-level computer vision tasks such as
change detection, object detection, traffic monitoring, border
surveillance, behavior analysis, video synopsis, action recognition,
anomaly detection, and object tracking, motion magnification, etc. In
literature, different modeling methods based on deep learning (CNNs,
GNNs) and graph signal processing concepts have been employed to
address the challenges of weather-specific applications (either
removal of rain, fog, snow, or haze) only. Nevertheless, only few
algorithms allow to handle these multi-weather conditions with a
unified network. Moreover, these algorithms require high computational
complexity, which leads to poor inference performance in real-world
scenarios, and also are most-of-the time not suitable in unseen
scenarios. In addition, very few algorithms are available for
simultaneous image/video restoration and static/moving object
detection in these challenging multi-weather scenarios.
Most of the time, these algorithms employ two-stage architectures to
address these challenges. In the first stage, an application-specific
image/video degrading algorithm is applied, and in the second stage,
high-level video processing tasks such as static/moving objects are
detected. Thus, there is an immense need to design and develop
end-to-end unified learning architectures which restore the
image/videos and detect the static/moving objects under sparse to
extreme multi-weather conditions.
Goal of This Workshop
The goals of this workshop are three-fold:
Designing unified framework that handles low- and high-level
computer vision applications such as intelligent transportation,
intelligent surveillance systems, conventional/aerial image or
video enhancements.
Proposing new algorithms that can fulfil the requirements of
real-time applications,
Proposing robust and interpretable deep learning to handle the key
challenges in pattern in these applications.
Broad Subject Areas for Submitting Papers
Papers are solicited to address deep learning methods to be applied in
based all-weather surveillance system,including but not limited to the
following:
Graph Machine Learning for Computer Vision
Transductive/Inductive Graph Neural Networks (GNNs)
GNNs Architectures
Zero-shot Learning
Graph Signal Processing for Computer Vision
Graph Spectral Clustering for Computer Vision
Ensemble learning-based methods
Meta-knowledge Learning methods
RGB-D cameras, Event based cameras
Important Dates
Full Paper Submission Deadline: August 30, 2024
Decisions to Authors: September 20, 2024
Camera-ready Deadline: Same than ACCV 2024.
Selected papers, after extensions and further revisions, will be
published in a special issue of an international journal.