HUMAN ACTION RECOGNITION VIA SPATIAL AND TEMPORAL METHODS

In this work human action recognition problem was discussed in video sequences. Solution of the problem was studied in three stages. Firstly, points of interest were detected with preproccesing and these points which are called cuboids were declared in small windows, then feature extraction was performed and finally, human action is decided by using classification. Features extraction is not only performed in the spatial domain but also along the cuboid video, that is in the time domain. K-nn(nearest neighbor) algorithm was used as a classifier. Furthermore, algorithm was run on the Weizmann database and results were presented. In addition to the traditional human action studies, different databases were evaluated in this work, preprocessing and feature extraction parameter optimization was also performed. The results show that performance is increased by 6-11%.