Recognizing action at a distance

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Recognizing action at a distance (2003)

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by Alexei A. Efros , Alexander C. Berg , Greg Mori , Jitendra Malik

Venue:	PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION
Citations:	504 - 20 self

BibTeX

@INPROCEEDINGS{Efros03recognizingaction,
    author = {Alexei A. Efros and Alexander C. Berg and Greg Mori and Jitendra Malik},
    title = {Recognizing action at a distance},
    booktitle = {PROCEEDINGS OF THE IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION},
    year = {2003},
    pages = {726--733},
    publisher = {}
}

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Abstract

Our goal is to recognize human actions at a distance, at resolutions where a whole person may be, say, 30 pixels tall. We introduce a novel motion descriptor based on optical flow measurements in a spatio-temporal volume for each stabilized human figure, and an associated similarity measure to be used in a nearest-neighbor framework. Making use of noisy optical flow measurements is the key challenge, which is addressed by treating optical flow not as precise pixel displacements, but rather as a spatial pattern of noisy measurements which are carefully smoothed and aggregated to form our spatio-temporal motion descriptor. To classify the action being performed by a human figure in a query sequence, we retrieve nearest neighbor(s) from a database of stored, annotated video sequences. We can also use these retrieved exemplars to transfer 2D/3D skeletons onto the figures in the query sequence, as well as two forms of data-based action synthesis “Do as I Do” and “Do as I Say”. Results are demonstrated on ballet, tennis as well as football datasets.

Keyphrases

query sequence noisy optical flow measurement video sequence noisy measurement human figure precise pixel displacement similarity measure novel motion descriptor data-based action synthesis spatio-temporal volume retrieved exemplar key challenge whole person spatial pattern football datasets optical flow spatio-temporal motion descriptor optical flow measurement human action stabilized human figure nearest-neighbor framework

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