WIDE AREA SCENE ANALYSIS IN VISION NETWORK

Camera Network Tracking and Re-identification

In many computer vision tasks it is often desirable to identify and monitor people as they move through a network of non-overlapping cameras. This is especially challenging as for a network of cameras issues such as changes of scale, illumination, viewing angle and pose start to arise. In this project we try to address this inter camera person association problem. We have shown that incorporating a consistency requirement of re-identification results across the camera network can significantly improve the re-identification performance even in the challenging scenarios where the illumination changes between the cameras are large.

Sample Publications

Distributed Estimation

In this project, we are developing methods for estimation and control in distributed camera networks. Specifically, we have looked at the following problems.

  • Distributed estimation algorithms that are aware of the specific constraints of camera networks
  • Distributed control for collaborative and opportunistic sensing in wide-area camera networks

Demos

Information-Weighted Consensus in a Distributed Camera Network

Cooperative and opportunistic imaging in a camera network

  • See demo video above.

Sample Publications

PhD Thesis


Demonstration video of PTZ camera network control in the UCR camera network facility using game-theoretic distributed optimization techniques. (Please use Firforx, Chrome or IE10)

Active Sensing

We are developing methods for optimizing the image acquisition capabilities of the cameras so as to maximize the performance of the methods used to analyze these image. This needs to be done through collaboration between the cameras in a network, as each camera's parameters entail constraints on the others. We have developed distributed control methods for collaborative and opportunistic sensing in wide-area camera networks.

Sample Publications

Parts of this work have been supported by the NSF CPS program under the project Distributed Sensing, Learning and Control in Dynamic Environments.