Towards Understanding Action Recognition

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...

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Bibliographic Details
Published in2013 IEEE International Conference on Computer Vision pp. 3192 - 3199
Main Authors Hueihan Jhuang, Gall, Juergen, Zuffi, Silvia, Schmid, Cordelia, Black, Michael J.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.12.2013
Subjects
Accuracy
action recognition
Algorithms
annotation
Computer vision
Conferences
dataset
Estimation
Ground truth
Human
JHMDB
Joints
Level (quantity)
Motion pictures
optical flow estimation
pose estimation
Recognition
Robustness
Trajectory
Vectors
Videos
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Summary: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 high-level 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 J-HMDB dataset should facilitate a deeper understanding of action recognition algorithms.
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ISSN:1550-5499
DOI:10.1109/ICCV.2013.396