Title: Human action recognition from RGBD videos based on retina model and local binary pattern features
Authors: Al-Akam, Rawya
Al-Darraji, Salah
Paulus, Dietrich
Citation: WSCG 2018: poster papers proceedings: 26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Visionin co-operation with EUROGRAPHICS Association, p. 1-7.
Issue Date: 2018
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
URI: wscg.zcu.cz/WSCG2018/!!_CSRN-2803.pdf
ISBN: 978-80-86943-42-8
ISSN: 2464-4617
Keywords: rozpoznání akce;RGBD videa;lokální binární vzory;model sítnice;náhodný les
Keywords in different language: action recognition;RGBD videos;local binary pattern;retina model;random forest
Abstract: Human action recognition from the videos is one of the most attractive topics in computer vision during the last decades due to wide applications development. This research has mainly focused on learning and recognizing actions from RGB and Depth videos (RGBD). RGBD is a powerful source of data providing the aligned depth information which has great ability to improve the performance of different problems in image understanding and video processing. In this work, a novel system for human action recognition is proposed to extract distinctive spatio and temporal feature vectors for presenting the spatio-temporal evolutions from a set of training and testing video sequences of different actions. The feature vectors are computed in two steps: The First step is the motion detection from all video frames by using spatio-temporal retina model. This model gives a good structuring of video data by removing the noise and illumination variation and is used to detect potentially salient areas, these areas represent the motion information of the moving object in each frame of video sequences. In the Second step, because of human motion can be seen as a type of texture pattern, the local binary pattern descriptor (LBP) is used to extract features from the spatio-temporal salient areas and formulated them as a histogram to make the bag of feature vectors. To evaluate the performance of the proposed method, the k-means clustering, and Random Forest classification is applied on the bag of feature vectors. This approach is demonstrated that our system achieves superior performance in comparison with the state-of-the-art and all experimental results are depending on two public RGBD datasets.
Rights: © Václav Skala - Union Agency
Appears in Collections:WSCG 2018: Poster Papers Proceedings

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