Deep metric learning autoencoder for nonlinear temporal alignment of human motion - Xiaochuan Yin; Qijun Chen (2016 IEEE International Conference on Robotics and Automation (ICRA), 2016)
Temporal alignment is an important preprocessing procedure for human action recognition. The challenge of temporal alignment problem is the temporal scale difference between human actions as well as the variability of each subject. Metric learning is the central problem of temporal alignment. This paper presents a nonlinear time alignment method with deep autoencoder. The spatio-temporal features obtained from the neural network contain the metric information for feature comparison. The effectiveness of our method is verified with k-nearest neighbor (k-NN) classifier on MSR-Action 3D and MSR-Daily Activity 3D datasets. Experimental results illustrate that the proposed method achieves superior performance to other metric based techniques.