Integrating Spatio-Temporal Context With Multiview Representation for Object Recognition in Visual Surveillance

—We present in this paper an integrated solution to rapidly recognizing dynamic objects in surveillance videos by exploring various contextual information. This solution consists of three components. The first one is a multi-view object representation. It contains a set of deformable object templates, each of which comprises an ensemble of active features for an object category in a specific view/pose. The template can be efficiently learned via a small set of roughly aligned positive samples without negative samples. The second component is a unified spatio-temporal context model, which integrates two types of contextual information in a Bayesian way. One is the spatial context, including main surface property (constraints on object type and density) and camera geometric parameters (constraints on object size at a specific location). The other is the temporal context, containing the pixel-level and instance-level consistency models, used to generate the foreground probability m...

• Type: Journal
• Year: 2011
• Where: TCSV
• Authors: Xiaobai Liu, Liang Lin, Shuicheng Yan, Hai Jin, Wenbing Tao
• Keywords: Contextual Information, Object Category, Spatio-temporal Context Model, TCSV 2011, Theoretical Computer Science