Spatiotemporal Features for Action Recognition and Salient Event Detection

Although the mechanisms of human visual understanding remain partially unclear, computational models inspired by existing knowledge on human vision have emerged and applied to several fields. In this paper, we propose a novel method to compute visual saliency from video sequences by counting in the actual spatiotemporal nature of the video. The visual input is represented by a volume in space--time and decomposed into a set of feature volumes in multiple resolutions. Feature competition is used to produce a saliency distribution of the input implemented by constrained minimization. The proposed constraints are inspired by and associated with the Gestalt laws. There are a number of contributions in this approach, namely extending existing visual feature models to a volumetric representation, allowing competition across features, scales and voxels, and formulating constraints in accordance with perceptual principles. The resulting saliency volume is used to detect prominent spatiotemporal regions and consequently applied to action recognition and perceptually salient event detection in video sequences. Comparisons against established methods on public datasets are given and reveal the potential of the proposed model. The experiments include three action recognition scenarios and salient temporal segment detection in a movie database annotated by humans.