Classification of Cinematographic Shots Using Lie Algebra and its Application to Complex Event Recognition

In this paper, we propose a discriminative representation of a video shot based on its camera motion and demonstrate how the representation can be used for high level multimedia tasks like complex event recognition. In our technique, we assume that a homography exists between a pair of subsequent frames in a given shot. Using purely image-based methods, we compute homography parameters that serve as coarse indicators of the ambient camera motion. Next, using Lie algebra, we map the homography matrices to an intermediate vector space that preserves the intrinsic geometric structure of the transformation. The mappings are stacked temporally to generate vector time-series per shot. To extract meaningful features from time-series, we propose an efficient linear dynamical system based technique. The extracted temporal features are further used to train linear SVMs as classifiers for a particular shot class. In addition to demonstrating the efficacy of our method on a novel dataset, we extend its applicability to recognize complex events in large scale videos under unconstrained scenarios. Our empirical evaluations on eight cinematographic shot classes show that our technique performs close to approaches that involve extraction of 3-D trajectories using computationally prohibitive structure from motion techniques.