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Human actions recognition: an approach based on stable motion boundary fields

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Abstract

Automatic video action recognition have been a long-standing problem in computer vision. To obtain a scalable solution for actions recognition, it is important to have efficient visual representation of motions. In this paper, we propose a new visual representation for actions based in the body motion boundaries. The first step, a set of optical flow frames highlighting the principal motions in the poses is substracted. Then, the motion boundaries are computed from the previous optical flow frames. Maximum Stable Extremal Regions are then applied to motion boundaries maps in order to obtain Motion Stable Shape (MSS) features. Local descriptors were computed based on each detected MSS to capture motion patterns. To predict the classes of the different human actions, we have represented different descriptors with a bag-of-words (BOW) model and for classification, we use a non-linear support vector machine. We have performed a set of experiments on different datasets: Weizmann, KTH, UCF sport, UCF50 and Hollywood to prove the efficiency of our developed model. The achieved results improve the state-of-the-art on the KTH and Weizmann datasets and are comparable to state-of-the-art for UCF sport and UCF50 datasets.

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  1. Limtic Laboratory, Institut Supérieur d’Informatique (ISI), Ariana, Tunisia

    Imen Lassoued & Ezzeddine Zagrouba

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  1. Imen Lassoued
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  2. Ezzeddine Zagrouba
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Correspondence to Imen Lassoued.

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Lassoued, I., Zagrouba, E. Human actions recognition: an approach based on stable motion boundary fields. Multimed Tools Appl 77, 20715–20729 (2018). https://doi.org/10.1007/s11042-017-5477-0

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