Laplacian LRR on Product Grassmann Manifolds for Human Activity Clustering in Multi-Camera Video Surveillance
This addresses the problem of efficient multi-camera video analysis for surveillance applications, but it appears incremental as it builds on existing manifold and LRR techniques.
The paper tackled the challenge of representing and fusing multi-camera video data for human activity clustering by proposing a novel representation on Product Grassmann Manifolds and extending the Low Rank Representation model, resulting in outperforming state-of-the-art clustering methods on several datasets.
In multi-camera video surveillance, it is challenging to represent videos from different cameras properly and fuse them efficiently for specific applications such as human activity recognition and clustering. In this paper, a novel representation for multi-camera video data, namely the Product Grassmann Manifold (PGM), is proposed to model video sequences as points on the Grassmann manifold and integrate them as a whole in the product manifold form. Additionally, with a new geometry metric on the product manifold, the conventional Low Rank Representation (LRR) model is extended onto PGM and the new LRR model can be used for clustering non-linear data, such as multi-camera video data. To evaluate the proposed method, a number of clustering experiments are conducted on several multi-camera video datasets of human activity, including Dongzhimen Transport Hub Crowd action dataset, ACT 42 Human action dataset and SKIG action dataset. The experiment results show that the proposed method outperforms many state-of-the-art clustering methods.