Open AccessAn Improved Method for Interest Point Detection in Human Activity Video
Author(s) -
Zhe Hu,
Hong Zhang,
Yang Yang,
Cheng-Fu Yang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1237/2/022089
Subject(s) - artificial intelligence , rgb color model , computer science , computer vision , pattern recognition (psychology) , feature (linguistics) , histogram , frame (networking) , similarity (geometry) , support vector machine , kernel (algebra) , intersection (aeronautics) , histogram equalization , mathematics , image (mathematics) , geography , telecommunications , philosophy , linguistics , cartography , combinatorics
An improved method for human activity recognition in RGB-D video using improved color-depth local spatio-temporal features (CoDe4D) detection method is presented in this paper. Firstly, histogram equalization and correction function are employed to suppress noise of rgb frame and depth frame, respectively. Then a saliency map is constructed by the improved CoDe4D method. In this way, feature points can be obtained by the saliency map, which integrates both depth information and RGB information. Next, the depth cuboid similarity feature (DCSF) is utilized to describe feature vectors. Meanwhile, visual words are generated by bag of feature method. To further improved the estimation accuracy, the SVM with generalized histogram intersection kernel is applied to train and predict categories. It shows good performance on MSR Daily Activity 3D datasets.