Minimum Class Variance Extreme Learning Machine for Human Action Recognition
Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas · IEEE Transactions on Circuits and Systems for Video Technology · 2013
In this paper, we propose a novel method aiming at view-independent human action recognition. Action description is based on local shape and motion information appearing at spatiotemporal locations of interest in a video. Action representation involves fuzzy vector quantization, while action classification is performed by a feedforward neural network. A novel classification algorithm, called minimum class variance extreme learning machine, is proposed in order to enhance the action classification performance. The proposed method can successfully operate in situations that may appear in real application scenarios, since it does not set any assumption concerning the visual scene background and the camera view angle. Experimental results on five publicly available databases, aiming at different application scenarios, denote the effectiveness of both the adopted action recognition approach and the proposed minimum class variance extreme learning machine algorithm.