Abstract:
Action recognition has become an important
research topic in the computer vision area. This
paper presents an efficient action recognition
approach based on salient object detection. Recently,
many features were directly extracted from video
frames; as a result, unsatisfying results were
produced due to intrinsic textural difference between
foreground and background. Instead of whole
frames, processing only on salient objects suppresses
the interference of background pixels and also makes
the algorithm to be more efficient. So, the main
contribution of this paper is to focus on salient object
detection to reflect textural difference. Firstly, salient
foreground objects are detected in video frames and
only interest features for such objects are detected.
Secondly, we extract features using SURF feature
detector and HOG feature descriptor. Finally, we use
KNN classifier for achieving better action
recognition accuracy. Experiments performed on
UCF-Sports action dataset show that our proposed
approach outperforms state-of-the-art action
recognition methods.
