Multi-view action synchronization in complex background

This paper addresses temporal synchronization of human actions under multiple view situation. Many researchers focused on frame by frame alignment for sync these multi-view videos, and expolited features such as interesting point trajectory or 3d human motion feature for event detecting individual. However, since background are complex and dynamic in real world, traditional image-based features are not fit for video representation. We explore the approach by using robust spatio-temporal features and self-similarity matrices to represent actions across views. Multiple sequences can be aligned their temporal patch(Sliding window) using the Dynamic Time Warping algorithm hierarchically and measured by meta-action classifiers. Two datasets including the Pump and the Olympic dataset are used as test cases. The methods are showed the effectiveness in experiment and suited general video event dataset.