Open AccessACTION RECOGNITION USING FEATURE TRANSFORM DESCRIPTOR FROM MINED DENSE SPATIO TEMPORAL
Author(s) -
Vikram Nathan,
P. M. Ashokkumar
Publication year - 2014
Publication title -
international journal of computer science and informatics
Language(s) - English
Resource type - Journals
ISSN - 2231-5292
DOI - 10.47893/ijcsi.2014.1148
Subject(s) - computer science , frame (networking) , artificial intelligence , pattern recognition (psychology) , preprocessor , a priori and a posteriori , feature (linguistics) , scale invariant feature transform , feature extraction , sequence (biology) , process (computing) , computer vision , data mining , philosophy , linguistics , telecommunications , epistemology , biology , genetics , operating system
Action recognition in video sequence has been a major challenging research area for number of years. Apriori algorithm and SIFT descriptor based approach for action recognition is proposed in this paper. Here, two phases can be carried out for accurate and updating of action recognition. In the first phase, the input should be the video sequence. For preprocessing the sequences frame can be formatted by background modeling for every successive frame. After modeling the background, the corners are detected for every frame and compound features are extracted. Data mining is performed by using Apriori algorithm as well as with the help of compound features extraction the action can be segregated in this video sequence. In the second phase, the same process is performed as well as analysis for new input frame and new pattern are updated for perfect recognition process. Due to Apriori algorithm, the processing delay is reduced and accuracy is improved.