Du Tran, Alexander Sorokin, and David Forsyth
This paper proposes a metric learning based approach for human activity
recognition with two main objectives: (1) reject unfamiliar
activities and
(2) learn with few examples. We show that our approach outperforms all
state-of-the-art methods on numerous standard
datasets for traditional action
classification problem. Furthermore, we demonstrate that our method not only can
accurately label activities
but also can reject unseen activities and can
learn from few examples with high accuracy. We finally show that our approach
works well on
noisy YouTube videos.
Video demonstrations:
Source code: Motion
context descriptor (MATLAB code with 2 sample sequences – 64
MB)
Computed features: for UIUC1, Weizmann, UMD, IXMAS (389MB)
Datasets
UIUC1
UIUC2
UIUC2: badmindton sequences (453 MB)
All annotations: forground masks, bounding boxes (101MB)
Publications
Du Tran, Alexander Sorokin,
Human Activity Recognition with Metric
Learning, ECCV08, Marseille, France.
Du Tran, Alexander Sorokin,
David Forsyth, Human Activity Recognition with Metric Learning,
UIUCDCS-R-2008-2952.