Abstract
Human action recognition and interpretation constitutes an important part of the video understanding. In this work, a novel action recognition system is developed that uses edge features obtained from optical flow power shapes which is represented as sequential gradient histograms. The presented system can achieve equal results to the complicated top action recognition systems of nowadays. The system is tested with the Weizmann dataset which is widely used in the field, and comparisons are given.
Keywords
Histogram
Action recognition
Computer science
Action (physics)
Artificial intelligence
Enhanced Data Rates for GSM Evolution
Optical flow
Pattern recognition (psychology)
Interpretation (philosophy)
Field (mathematics)
Computer vision
Image (mathematics)
Mathematics
Subject Areas
Human Pose and Action Recognition
·Computer Vision and Pattern Recognition ·Physical Sciences
Gait Recognition and Analysis
·Biomedical Engineering ·Physical Sciences
Hand Gesture Recognition Systems
·Human-Computer Interaction ·Physical Sciences
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