no code implementations • ICCV 2017 • Khurram Soomro, Mubarak Shah
Once classes are discovered, training videos within each cluster are selected to perform automatic spatio-temporal annotations, by first oversegmenting videos in each discovered class into supervoxels and constructing a directed graph to apply a variant of knapsack problem with temporal constraints.
no code implementations • 4 Dec 2016 • Khurram Soomro, Haroon Idrees, Mubarak Shah
For online prediction of action (interaction) confidences, we propose an approach based on Structural SVM that operates on short video segments, and is trained with the objective that confidence of an action or interaction increases as time progresses.
no code implementations • CVPR 2016 • Khurram Soomro, Haroon Idrees, Mubarak Shah
This paper proposes a novel approach to tackle the challenging problem of 'online action localization' which entails predicting actions and their locations as they happen in a video.
no code implementations • ICCV 2015 • Khurram Soomro, Haroon Idrees, Mubarak Shah
Context relations are learned during training which capture displacements from all the supervoxels in a video to those belonging to foreground actions.
7 code implementations • 3 Dec 2012 • Khurram Soomro, Amir Roshan Zamir, Mubarak Shah
To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips.
Ranked #5 on Action Recognition In Videos on UCF101
Action Recognition In Videos Skeleton Based Action Recognition +1