The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection

Mihai Zanfir, Marius Leordeanu, Cristian Sminchisescu; The IEEE International Conference on Computer Vision (ICCV), 2013, pp. 2752-2759

Abstract


Human action recognition under low observational latency is receiving a growing interest in computer vision due to rapidly developing technologies in human-robot interaction, computer gaming and surveillance. In this paper we propose a fast, simple, yet powerful non-parametric Moving Pose (MP) framework for low-latency human action and activity recognition. Central to our methodology is a moving pose descriptor that considers both pose information as well as differential quantities (speed and acceleration) of the human body joints within a short time window around the current frame. The proposed descriptor is used in conjunction with a modified kNN classifier that considers both the temporal location of a particular frame within the action sequence as well as the discrimination power of its moving pose descriptor compared to other frames in the training set. The resulting method is non-parametric and enables low-latency recognition, one-shot learning, and action detection in difficult unsegmented sequences. Moreover, the framework is real-time, scalable, and outperforms more sophisticated approaches on challenging benchmarks like MSR-Action3D or MSR-DailyActivities3D.

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[bibtex]
@InProceedings{Zanfir_2013_ICCV,
author = {Zanfir, Mihai and Leordeanu, Marius and Sminchisescu, Cristian},
title = {The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {December},
year = {2013}
}