Organizers


Sergio Escalera

Computer Vision Center (UAB) and University of Barcelona, Spain

sergio.escalera.guerrero@gmail.com

Sergio Escalera is Full Professor at the Department of Mathematics and Informatics, Universitat de Barcelona, where he is the head of the Informatics degree. He is ICREA Academia. He leads the Human Pose Recovery and Behavior Analysis Group. He is Distinguished Professor at Aalborg University. He is vice-president of ChaLearn Challenges in Machine Learning, leading ChaLearn Looking at People events. He is also Fellow of the ELLIS European Laboratory for Learning and Intelligent Systems working within the Human-centric Machine Learning program. He participated in several international funded projects and received an Amazon Research Award. He has published more than 300 research papers and received a CVPR best paper award nominee and a CVPR outstanding reviewer award.

Mickael Cormier

Fraunhofer IOSB and Karlsruhe Institute of Technology (KIT)fer IOSB, Germany

mickael.cormier@iosb.fraunhofer.de

Mickael Cormier received his M.Sc. degree in Computer Science from KIT in 2019, Germany. Currently, he is a PhD student at the Karlsruhe Institute of Technology and a research assistant at the Fraunhofer Institute for Optronics, System Technologies and Image Exploitation IOSB in Karlsruhe (Germany), which is one of the largest research institutes in the field of image acquisition, processing and analysis in Europe. He works in the group Video-assisted Security and Assistance Systems which focuses on the development of real-time video image evaluation in the context of distributed sensor systems. His research interests include, among others, human pose estimation and tracking, skeleton-based activity recognition and data annotation processes.

Kamal Nasrollahi

Milestone Systems and Aalborg University, Denmark

kn@create.aau.dk

Kamal Nasrollahi got his PhD on computer vision and machine learning in 2011 from Aalborg University in Denmark. He is currently working as Director of Research at Milestone Systems A/S and Professor of Computer Vision and Machine Learning at Aalborg University. Kamal has published more than 130 papers on various topics, like object detection, tracking, anomaly detection, and image enhancement.

Andreas Specker

Karlsruhe Institute of Technology (KIT), Germany and Fraunhofer IOSB

andreas.specker@iosb.fraunhofer.de

Andreas Specker completed his master's degree in computer science at the Karlsruhe Institute of Technology (KIT) in 2018. Since the end of 2018, he has been employed as a research assistant and Ph.D. student at KIT's Vision and Fusion Laboratory (IES). Mr. Specker works in close cooperation with the Video Exploitation Systems department of Fraunhofer IOSB in Karlsruhe, Germany. His research focuses on deep learning-based methods for attribute-based person retrieval and multi-camera tracking.

Julio C. S. Jacques Junior

University of Barcelona (UB), Spain

juliojj@gmail.com

Julio C. S. Jacques Junior is an assistant professor at University of Barcelona (UB) and a research collaborator within Computer Vision Center (CVC). Member of the Human Pose Recovery and Behavior Analysis (HUPBA) group, he also collaborates within within ChaLearn and ChaLearn (LAP) Looking at People. He helped to organize workshops and challenges at high impact conferences (e.g., NeurIPS, CVPR, ECCV, ICCV). His research interests include, among others, computer vision-based applications with a particular focus on visual human behavior analysis.

Jürgen Beyerer

Karlsruhe Institute of Technology (KIT), Germany and Fraunhofer IOSB

jurgen.beyerer@iosb.fraunhofer.de

Jürgen Metzler

Fraunhofer IOSB, Germany

juergen.metzler@iosb.fraunhofer.de

News


WACV'23 Pedestrian Attribute Recognition and Attributed-based Person Retrieval Challenge

The ChaLearn WACV'23 Pedestrian Attribute Recognition and Attributed-based Person Retrieval Challenge has just opened on Codalab. Join us to push the boundaries of pedestrian attribute recognition and attributed-based person retrieval along with concept drift on an extension of the UPAR dataset.