Abstract:
Object Movement Identification from videos is very challenging, and has got
numerous applications in sports evaluation, video surveillance, elder/child care, etc. In
thisresearch, a model using sparse representation is presented for the human activity detection
from the video data. This is done using a linear combination of atoms from a dictionary and a
sparse coefficient matrix. The dictionary is created using a Spatio Temporal Interest Points
(STIP) algorithm. The Spatio temporal features are extracted for the training video data as well
as the testing video data. The K-Singular Value Decomposition (KSVD)algorithm is used for
learning dictionaries for the trainingvideo dataset. Finally, human action is classified using
aminimum threshold residual value of the corresponding actionclass in the testing video dataset.
Experiments are conducted onthe KTH dataset which contains a number of actions. Thecurrent
approach performed well in classifying activities with asuccess rate of 90%.
