This paper proposes a boosting EigenActions algorithm for human action recognition. A spatio-temporal Information Saliency Map (ISM) is calculated from a video sequence by estimating pixel density function. A continuous human action is segmented into a set of primitive periodic motion cycles from information saliency curve. Each cycle of motion is represented by a Salient Action Unit (SAU), which is used to determine the EigenAction using principle component analysis. A human action classifier is developed using multi-class Adaboost algorithm with Bayesian hypothesis as the weak classifier. Given a human action video sequence, the proposed method effectively locates the SAUs in the video, and recognizes the human actions by categorizing the SAUs. Two publicly available human action databases, namely KTH and Weizmann, are selected for evaluation. The average recognition accuracy are 81.5% and 98.3% for KTH and Weizmann databases, respectively. Comparative results with two recent methods and robustness test results are also reported.
Scopus Subject Areas
- Signal Processing
- Computer Vision and Pattern Recognition
- Human action recognition
- Salient action unit