Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  revisits the problem of human action recognition from skeleton joint locations, and analyses the tradeoff of sampling the joint space with respect to the recognition performance and computational complexity. The provided insights led to the design of a new algorithm for automatically selecting the most appropriate set of joints for each action. During the training stage, the approach applies a guided joint sampling strategy for learning different SVM classifiers, selecting the classifier that maximizes confidence and ambiguity metrics. Experimental results on three action datasets show that pre-selecting the most varying skeleton joints for each action dramatically reduces the computational complexity while keeping competitive recognition rates.
['Michel Goncalves Almeida Antunes', 'Djamila Aouada', 'Björn E. Ottersten']
I am writing a research paper on a idea A revisit to human action recognition from depth sequences: Guided SVM-sampling for joint selection
[{"from": "human", "value": "I am writing a research paper on a idea A revisit to human action recognition from depth sequences: Guided SVM-sampling for joint selection"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  revisits the problem of human action recognition from skeleton joint locations, and analyses the tradeoff of sampling the joint space with respect to the recognition performance and computational complexity. The provided insights led to the design of a new algorithm for automatically selecting the most appropriate set of joints for each action. During the training stage, the approach applies a guided joint sampling strategy for learning different SVM classifiers, selecting the classifier that maximizes confidence and ambiguity metrics. Experimental results on three action datasets show that pre-selecting the most varying skeleton joints for each action dramatically reduces the computational complexity while keeping competitive recognition rates."}]