We propose a novel approach for modelling correlations between activities in a busy public
space captured by multiple non-overlapping and uncalibrated cameras. In our approach …

We propose a novel approach to understanding activities from their partial observations
monitored through multiple non-overlapping cameras separated by unknown time gaps. In …

The paper investigates the unsupervised learning of a model of activity for a multi-camera
surveillance network that can be created from a large set of observations. This enables the …

In many surveillance applications it is desirable to determine if a given individual has been
previously observed over a network of cameras. This is the person reidentification problem …

This paper presents an approach for reconstruction of 4D temporally coherent models of
complex dynamic scenes. No prior knowledge is required of scene structure or camera …

Presents a methodology for automatically identifying human action. We use a new approach
to human activity recognition that incorporates a Bayesian framework. By tracking the …

We introduce a behavior-based similarity measure which tells us whether two different
space-time intensity patterns of two different video segments could have resulted from a …

We present a framework for detecting and recognizing human activities for outdoor video
surveillance applications. Our research makes the following contributions: For activity …

Processing of image sequences has progressed from simple structure from motion paradigm
to the recognition of actions/interactions as events. Understanding human activities in video …

This paper describes a system for real-time tracking of people in video sequences. The input
to the system is live or recorded video data acquired by a stationary camera in an …