Distributed surveillance

Objective

In this project we develop techniques for tracking multiple objects with multiple cameras. In particular, we focus on problems where the cameras are sparsely distributed over a wide area (think of an airport, shopping mall or a motorway). In this cases, the effective fields of view the cameras remain disjoint. When an object (like a person or a car) leaves the view of some camera, we temporarily loose track of it. The key goal of this project is re-identification of that object whenever it later on reappears in the view of some other camera. In this way we can recover the global trajectories of objects in the considered area.

Research group members

Funding

Research Achievements

We have developed an online data association algorithm based on Bayesian inference in Infinite Gaussian Mixture Models (also known as Dirichlet Process Mixture Models). When an object leaves the view of one camera, and after some time, enters the view of another camera, we want to associate the two local trajectories with a single global trajectory. Each local trajectory consists of multiple frames, therefore we compress it into a set of features that describe appearance of the object (like color distribution) and spatio-temporal features (like direction, or entry/leave timestamps). The feature-based association is a common problem in multi-object tracking. However, the standard methods are not immediately applicable as the motion of object is not smooth (because of the gap between fields of view). See the demo or read more in the ICPR 2004 paper.

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