Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Action recognition has often been posed as a classification problem, which assumes that a video sequence only have one action class label and different actions are independent. Hover, a single human body can perform multiple concurrent actions at the same time, and different actions interact with each other.  proposes a concurrent action detection model where the action detection is formulated as a structural prediction problem. In this model, an interval in a video sequence can be described by multiple action labels. An detected action interval is determined both by the unary local detector and the relations with other actions.  use a wavelet feature to represent the action sequence, and design a composite temporal logic descriptor to describe the action relations. The model parameters are trained by structural SVM learning. Given a long video sequence, a sequential decision window search algorithm is designed to detect the actions. Experiments on our new collected concurrent action dataset demonstrate the strength of our method.
['Ping Wei', 'Nanning Zheng', 'Yibiao Zhao', 'Song-Chun Zhu']
I am writing a research paper on a idea Concurrent Action Detection with Structural Prediction
[{"from": "human", "value": "I am writing a research paper on a idea Concurrent Action Detection with Structural Prediction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Action recognition has often been posed as a classification problem, which assumes that a video sequence only have one action class label and different actions are independent. Hover, a single human body can perform multiple concurrent actions at the same time, and different actions interact with each other.  proposes a concurrent action detection model where the action detection is formulated as a structural prediction problem. In this model, an interval in a video sequence can be described by multiple action labels. An detected action interval is determined both by the unary local detector and the relations with other actions.  use a wavelet feature to represent the action sequence, and design a composite temporal logic descriptor to describe the action relations. The model parameters are trained by structural SVM learning. Given a long video sequence, a sequential decision window search algorithm is designed to detect the actions. Experiments on our new collected concurrent action dataset demonstrate the strength of our method."}]