Multi-camera real-time three-dimensional tracking of multiple flying animals.

Journal of the Royal Society, Interface
Andrew D StrawMichael H Dickinson

Abstract

Automated tracking of animal movement allows analyses that would not otherwise be possible by providing great quantities of data. The additional capability of tracking in real time--with minimal latency--opens up the experimental possibility of manipulating sensory feedback, thus allowing detailed explorations of the neural basis for control of behaviour. Here, we describe a system capable of tracking the three-dimensional position and body orientation of animals such as flies and birds. The system operates with less than 40 ms latency and can track multiple animals simultaneously. To achieve these results, a multi-target tracking algorithm was developed based on the extended Kalman filter and the nearest neighbour standard filter data association algorithm. In one implementation, an 11-camera system is capable of tracking three flies simultaneously at 60 frames per second using a gigabit network of nine standard Intel Pentium 4 and Core 2 Duo computers. This manuscript presents the rationale and details of the algorithms employed and shows three implementations of the system. An experiment was performed using the tracking system to measure the effect of visual contrast on the flight speed of Drosophila melanogaster. At low con...Continue Reading

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Citations

Jul 13, 2011·Proceedings of the National Academy of Sciences of the United States of America·Allison F Carey, John R Carlson
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Methods Mentioned

BETA
feature extraction

Software Mentioned

matplotlib
wx
cython
y GL
- Camera Self Calibration Toolbox
gcc
libdc
scipy
Camera Calibration Toolbox
pyglet

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