Human Activity Recognition

Towards Richer Video Representation for Action Understanding

Humam Alwassel, Ph.D. Student, Computer Science

Jan 23, 18:30 - 20:30

B2 L5 R5209

Computer Vision machine learning Human Activity Recognition

With video data dominating the internet traffic, it is crucial to develop automated models that can analyze and understand what humans do in videos. Such models must solve tasks such as action classification, temporal activity localization, spatiotemporal action detection, and video captioning. This dissertation aims to identify the challenges hindering the progress in human action understanding and propose novel solutions to overcome these challenges.

Humam Alwassel

Ph.D. Student, Computer Science

Computer Vision machine learning Human Activity Recognition

Humam Alwassel is a Computer Science Ph.D. candidate in Image and Video Understanding Lab (IVUL) Group under the supervision of Professor Bernard Ghanem at King Abdullah University of Science and Technology (KAUST). Education and Early Career Humam obtained his bachelor degree with double major in Computer Science and Mathematics from Cornell University in New York, USA in 2016. After that, he joined KAUST for the MS/PhD program and received his master degree in Computer Science in 2018. Research Interest He’s currently focused on the development of novel computer vision techniques for video