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Human action recognition has great potential in many applications relevant to artificial intelligence, which can accelerate some research on expert and intelligent systems, such as feature selection. To improve the performance on human action recognition in realistic scenarios, a novel Salient Foreground Trajectory extraction method based on saliency detection and low-rank matrix recovery is proposed...
A new approach to human action recognition from realistic videos is presented in this paper. First, an affine motion model is utilized to compensate background motion for the purpose of extracting dense foreground trajectories. Then, a trajectory spectral embedding is introduced to split up foreground action into multiple spatio-temporal action parts for constructing a mid-level representation. To...
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