Open AccessVector space based augmented structural kinematic feature descriptor for human activity recognition in videos
Author(s) -
Dharmalingam Sowmiya,
Palanisamy Anandhakumar
Publication year - 2018
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2018-0102
Subject(s) - artificial intelligence , support vector machine , pattern recognition (psychology) , feature vector , robustness (evolution) , action recognition , kinematics , computer science , activity recognition , representation (politics) , computer vision , mathematics , class (philosophy) , physics , classical mechanics , gene , biochemistry , chemistry , politics , political science , law
A vector space based augmented structural kinematic ( VSASK ) feature descriptor is proposed for human activity recognition. An action descriptor is built by integrating the structural and kinematic properties of the actor using vector space based augmented matrix representation. Using the local or global information separately may not provide sufficient action characteristics. The proposed action descriptor combines both the local (pose) and global (position and velocity) features using augmented matrix schema and thereby increases the robustness of the descriptor. A multiclass support vector machine ( SVM ) is used to learn each action descriptor for the corresponding activity classification and understanding. The performance of the proposed descriptor is experimentally analyzed using the Weizmann and KTH datasets. The average recognition rate for the Weizmann and KTH datasets is 100% and 99.89%, respectively. The computational time for the proposed descriptor learning is 0.003 seconds, which is an improvement of approximately 1.4% over the existing methods.