ONFIRE Contest 2025 Call for Papers

=== Call for submissions ===
ONFIRE Contest 2025
23rd International Conference on Image Analysis and Processing (ICIAP)

Conference Website: https://sites.google.com/view/iciap25/home?authuser=0
Contest Website: https://mivia.unisa.it/onfire2025/
========================

=== Important dates ===
Method Submission Deadline: 6th June, 2025
Contest Paper Deadline: 13th June, 2025
========================

=== Contest ===
Throughout history, societies have faced fire-related risks, which
intensified during the industrial era due to machinery malfunctions
and misuse. Today, fire remains a major threat to human life,
infrastructure and ecosystems. To prevent disasters and protect the
environment, authorities are turning to advanced surveillance systems
powered by Computer Vision algorithms for automatic, reliable fire
detection. Early Computer Vision approaches, based on color and motion
models, struggled with the variability of real-world scenes. The
introduction of Machine Learning and Deep Learning techniques
significantly improved detection performance, though challenges
persist due to the complex nature of fire phenomena and limitations in
available datasets. Detection failures often occur when fires appear
differently from the training samples, for example when visible from
greater distances or when moving objects resembling fire confuse the
system, leading to false alarms. A review of the literature highlights
two main gaps in current methods. The first concerns the need to
design detection systems according to the application scenarios. While
well-trained, frame-based detectors perform effectively in simple
situations where flames or smoke are clearly visible and no other
moving objects are present, more complex scenarios — such as when
flames are small or numerous moving objects resemble fire — require
sophisticated models incorporating temporal analysis
techniques. Enhancing methods with scenario awareness and tailoring
them to specific operational conditions can significantly improve
real-world performance. The second gap relates to achieving an optimal
balance between precision and recall. Although current methods show
good sensitivity in detecting fires (high recall), they often lack
precision in distinguishing fire from visually similar objects. This
issue was also evident during the first ONFIRE 2023 contest, where
even top-performing systems generated excessive false alarms,
undermining operational reliability and increasing costs due to the
need for human intervention. In this context, the ONFIRE 2025
international competition has been launched to foster the development
of advanced, real-time fire detection algorithms for fixed CCTV
cameras, deployable on smart cameras or embedded systems with limited
resources. The contest challenges participants to create solutions
that address these limitations across four application scenarios of
varying difficulty:

- Low Activity - Short Range (easy)
- Low Activity - Long Range (intermediate)
- High Activity - Short Range (difficult)
- High Activity - Long Range (intermediate)

Each method will be evaluated on a private test set of unseen,
scenario-categorized videos and ranked both overall and by
scenario. Additionally, frame processing speed and memory usage will
be assessed to ensure efficiency and resource compatibility. A final
score, combining F1-score with resource consumption, will determine
the official rankings. Competitors will work with an expanded dataset
compared to ONFIRE 2023, featuring over 300 annotated videos from
public sources, with the option to incorporate additional publicly
available data. A reference baseline will also be provided for
performance comparison.

The detailed description can be read here: https://mivia.unisa.it/onfire2025/
========================

=== Rules ===
The deadline for the submission of the methods is 6th June, 2025. The
submission must be done with an email in which the participants share
(directly or with external links) the trained model, the code and the
report. The participants can receive the training set and its
annotations by sending an email, in which they also communicate the
name of the team. The participants can use these training samples and
annotations but also additional videos. The participants are strongly
encouraged to submit a contest paper by the deadline of 13th June,
2025. The paper can be submitted through Easychair. The maximum number
of pages is 12 including references. Accepted papers will be included
in the ICIAP 2025 Workshops Proceedings.

The detailed instructions can be read here: https://mivia.unisa.it/onfire2025/
========================

The organizers,

Diego Gragnaniello, University of Salerno, Italy
Antonio Greco, University of Salerno, Italy
Carlo Sansone, University of Naples - Federico II, Italy
Bruno Vento, University of Naples - Federico II, Italy