Patterns of motion in non-overlapping networks using vehicle tracking data

We present a systematic framework to learn motion patterns based on vehicle tracking data captured by multiple non-overlapping uncalibrated cameras. We assume that the tracks from individual cameras are available. We define the key problems related to the multi-camera surveillance system and present solutions to these problems: learning the topology of the network, constructing tracking correspondences between different views, learning the activity clusters over global views and finally detecting abnormal events. First, we present a weighted cross correlation model to learn the topology of the network without solving correspondence in the first place. We use estimates of normalized color and apparent size to measure similarity of object appearance between different views. This information is used to temporally correlated observations, allowing us to infer possible links between disjoint views, and to estimate the associated transition time. Based on the learned cross correlation coefficient, the network topology can be fully recovered. Then, we present a MAP framework to match two objects along their tracks from non overlapping camera views and discuss how the learned topology can reduce the correspondence search space dramatically. We propose to learn the color transformation in [iota][alpha][beta] space to compensate for the varying illumination conditions across different views, and learn the inter-camera time transition and the shape/size transformation between different views.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Niu, Chaowei

Advisor dc:contributor.advisor

• W. Eric L. Grimson.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/58278

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/58278