Abstract
The problem of extracting feature from unconstrained videos for representing human actions has been investigated in order to recognize human actions in complex environment in this paper.Firstly,morphological gradient was used to eliminate most background information.Then,edge of shape was extracted and accumulated to a frame,which was named accumulative edge image(AEI).Grid-based histograms of orientation gradients(HOG) were calculated and formed a feature vector that captured the characteristic of human actions in this video sequence.Using support vector machine(SVM),the method was tested on the YouTube action dataset.The obtained impressive results showed that this method was more effective than other methods in YouTube action dataset.
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