Visualization and clustering of crowd video content in MPCA subspace

Abstract

This paper presents a novel approach for the visualization and clustering of crowd video contents by using multilinear principal component analysis (MPCA). In contrast to feature-point-based approach and frame-based dimensionality reduction approach, the proposed method maps each short video segment to a point in MPCA subspace to take temporal information into account naturally through tensorial representations. Specifically, MPCA projects each short segment of a video to a low-dimensional tensor first. A few MPCA features are then selected according to the variance captured as the final representation. Thus, a video is visualized as a trajectory in MPCA subspace. The trajectory generated enables visual interpretation of video content in a compact space as well as visual clustering of video events. The proposed method is evaluated on the PETS 2009 datasets through comparison with three existing methods for video visualization. The MPCA visualization shows superior performance in clustering segments of the same event as well as identifying the transitions between events.

Publication
ACM International Conference on Information and Knowledge Management (CIKM)
Haiping Lu
Haiping Lu
Professor of Machine Learning, Head of AI Research Engineering, and Turing Academic Lead

I am a Professor of Machine Learning. I develop translational AI technologies for better analysing multimodal data in healthcare and beyond.