Tracking multiple mice

Monitoring mouse social behaviors over long periods of time is essential for neurobehavioral analysis of social mouse phenotypes. Currently, the primary method of social behavioral plienotyping utilizes human labelers, which is slow and costly. In order to achieve the high throughput desired for scientific studies, social behavioral phenotyping must be automated. The problem of automation can be divided into two tasks; tracking and phenotyping. First, individual body parts of mice must be accurately tracked. This is achieved using shape context descriptors to obtain precise point to point correspondences between templates and mice in any frame of a video. This method provides for greater precision and accuracy than current state of the art techniques. We propose a means by which this tracking information can be used to classify social behaviors between mice.

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

2012

Author dc:creator

• Braun, Stav

Advisor dc:contributor.advisor

• Tomaso Poggio.

• 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/1721.1/77001

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/77001