Towards understanding action recognition

Abstract : Although action recognition in videos is widely studied, current methods often fail on real-world datasets. Many recent approaches improve accuracy and robustness to cope with challenging video sequences, but it is often unclear what affects the results most. This paper attempts to provide insights based on a systematic performance evaluation using thoroughly-annotated data of human actions. We annotate human Joints for the HMDB dataset (J-HMDB). This annotation can be used to derive ground truth optical flow and segmentation. We evaluate current methods using this dataset and systematically replace the output of various algorithms with ground truth. This enables us to discover what is important - for example, should we work on improving flow algorithms, estimating human bounding boxes, or enabling pose estimation? In summary, we find that highlevel pose features greatly outperform low/mid level features; in particular, pose over time is critical, but current pose estimation algorithms are not yet reliable enough to provide this information. We also find that the accuracy of a top-performing action recognition framework can be greatly increased by refining the underlying low/mid level features; this suggests it is important to improve optical flow and human detection algorithms. Our analysis and JHMDB dataset should facilitate a deeper understanding of action recognition algorithms.
Document type :
Conference papers
Complete list of metadatas

Cited literature [34 references]  Display  Hide  Download


https://hal.inria.fr/hal-00906902
Contributor : Thoth Team <>
Submitted on : Tuesday, December 10, 2013 - 2:18:31 PM
Last modification on : Thursday, February 7, 2019 - 4:16:56 PM
Long-term archiving on : Friday, March 14, 2014 - 9:26:53 AM

Files

jhuangICCV2013.pdf
Publisher files allowed on an open archive

Identifiers

Collections

Citation

Hueihan Jhuang, Jurgen Gall, Silvia Zuffi, Cordelia Schmid, Michael J. Black. Towards understanding action recognition. ICCV - IEEE International Conference on Computer Vision, Dec 2013, Sydney, Australia. pp.3192-3199, ⟨10.1109/ICCV.2013.396⟩. ⟨hal-00906902⟩

Share

Metrics

Record views

3028

Files downloads

1614