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A compact descriptor CHOG3D and its application in human action recognition
Author(s) -
Ji Yanli,
Shimada Atsushi,
Nagahara Hajime,
Taniguchi Rinichiro
Publication year - 2013
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.21793
Subject(s) - histogram , computation , computer science , action recognition , artificial intelligence , pattern recognition (psychology) , action (physics) , dimension (graph theory) , histogram of oriented gradients , point (geometry) , feature (linguistics) , feature vector , algorithm , key (lock) , detector , motion (physics) , image (mathematics) , mathematics , geometry , physics , linguistics , philosophy , quantum mechanics , pure mathematics , class (philosophy) , telecommunications , computer security
In this paper, we propose a new method to calculate local features. We extend the FAST corner detector to the spatiotemporal space to extract the shape and motion information of human actions. And a compact peak‐kept histogram of oriented spatiotemporal gradients (CHOG3D) is proposed to calculate local features. CHOG3D is calculated in a small support region of a feature point, and it employs the simplest gradient, the first‐order gradient, for descriptor calculation. Through parameter training, the proper length of the CHOG3D is determined to be 80 elements, which is 1/12.5 times the dimension of HOG3D in the KTH dataset. In addition, it keeps the peak value of quantized gradient to represent human actions more exactly and distinguish them more correctly. CHOG3D overcomes the disadvantages of the complex calculation and huge length of the descriptor HOG3D. From a comparison of the computation cost, CHOG3D is 7.56 times faster than HOG3D in the KTH dataset. We apply the algorithm for human action recognition with support vector machine. The results show that our method outperforms HOG3D and some other currently used algorithms. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.