Multi-View Semantic Temporal Video Segmentation

In this work, we propose a multi-view temporal video segmentation approach that employs a Gaussian scoring process for determining the best segmentation positions. By exploiting the semantic action information that the dense trajectories video description offers, this method can detect intra-shot actions
as well, unlike shot boundary detection approaches. We compare the temporal segmentation results of the proposed method to both single-view and multi-view methods, and also compare the action recognition results obtained on ground truth video segments to the ones obtained on the proposed multi-view segments, on the IMPART multi-view action data set.