Call for Papers

7th Workshop on Real-World Surveillance 
7th Real-World Surveillance: Applications and Challenges
in conjunction with WACV 2027 
Orlando, FL, USA. 
https://vap.aau.dk/rws/
Either January 4 or 5, 2027
Paper deadline is October 16, 11:59 PM AoE. 

Computer vision models trained on public datasets often exhibit
performance drift when deployed in real-world surveillance, diverging
from their reported test-set results. This workshop invites papers
presenting experimental results from any real-world surveillance or
asset-protection application (e.g., guarding buildings and critical
infrastructure), the challenges encountered, and strategies for
mitigation on topics including, but not limited to:

    Object detection
    Tracking
    Action recognition
    Scene understanding
    Super-resolution
    Multi-modal surveillance

Furthermore, the workshop has a special attention to legal and ethical
issues of computer vision applications in real-world scenarios. We
therefore also welcome papers describing their methodology and
experimental results on legal matters (like GDPR, AI Act, and US
Executive Order on AI) or ethical concerns (like detecting bias
towards gender, race, or other characteristics and mitigating
strategies). We particularly encourage submissions addressing safety,
reliability, and regulatory compliance for critical infrastructure
protection, as well as privacy-preserving approaches in high-security
environments.

 

Important Dates

Paper submission deadline: October 16, 2026 (11:59 PM, AoE)
Paper submission deadline for challenge participants: October 23, 2026 (11:59 PM, AoE)
Decision notification: October 30, 2026
Camera-ready: November 17, 2026

 

Submission

Submitted papers are handled via OpenReview accessible here
Paper template and guidelines for the workshop are similar to those of
WACV and can be found here
https://openreview.net/group?id=thecvf.com/WACV/2027/Workshop/RWS
 

Challenge

UPAR Challenge 2027: Attributes, Retrieval, and Pose Estimation

RWS introduces a new edition of the UPAR Challenge, extending the
previous editions on Pedestrian Attribute Recognition (PAR) and
Attribute-Based Person Retrieval (ABPR) towards unified pedestrian
understanding. The challenge builds upon the UPAR benchmark by
incorporating Human Pose Estimation (HPE), enabling evaluation of
pedestrian appearance and body structure understanding across diverse
surveillance domains. The challenge consists of three tracks.

The first track continues PAR under challenging domain shifts, while
the second extends ABPR to cross-dataset pedestrian retrieval.  The
third track introduces surveillance-oriented HPE, evaluating keypoint
localization under variations in viewpoint, resolution, illumination,
and occlusion using the UPAR-Pose benchmark. A development phase for
method validation and a test phase with submission limits to avoid
overfitting are included. Baseline methods for all tracks will be
provided.

 

Best regards,

Andreas