Human action recognition using time-invariant key-trajectories describing spatio-temporal salient motion
Human action recognition (HAR) has been attracting much attention in the computer vision arena. In particular, many research efforts were dedicated for developing discriminative feature extraction methods for improving the HAR performance. Among them, trajectory-based features have shown state-of-the-art performance. However, the time-variance of trajectory-based feature and the large number of indistinctive trajectories describing the human action can limit their performance. In this paper, we propose extracting human action features from a distinctive subset of trajectories, namely key-trajectories. Moreover, the key-trajectories are extracted in a time-invariant manner, so that they are able to represent human action regardless of the time at which the action occurs. With publically available and challenging datasets, comparative experiments have been conducted. Results show that the proposed key-trajectory feature extraction improves the HAR performance thanks to their distinctive and time-invariant characteristics.