Gesture recognition in cooking video based on image features and motion features using Bayesian Network classifier
نویسندگان
چکیده
This paper proposes a method combining image features and motion features for gesture recognition in cooking video. By using image features including global and local features of RGB images and then representing those using bag of features, motions in video are represented. We also use relative positions between objects after they are detected in this frame. Motions are also represented through motion features calculated from frame sequences using dense trajectories. After all, we combine both image features and motion features to describe cooking gestures. We use Bayesian Network model to predict which action class a certain frame belongs to base on the action class of previous frames and the cooking gesture in current frame. Our method has been approved through ACE dataset that it can recognize human cooking action as we expected. In addition, it is also a general method for solving cooking actions recognition problem..
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