Multiple ant tracking with global foreground maximization and variable target proposal distribution

Motion and behavior analysis of social insects such as ants requires tracking many ants over time. This process is highly labor-intensive and tedious. Automatic tracking is challenging as ants often interact with one another, resulting in frequent occlusions that cause drifts in tracking. In additio...

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Bibliographic Details
Published in2011 IEEE Workshop on Applications of Computer Vision (WACV) pp. 570 - 576
Main Authors Fletcher, Mary, Dornhaus, Anna, Shin, Min C
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2011
Subjects
Color
Markov processes
Mathematical model
Pixel
Proposals
Target tracking
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Summary:Motion and behavior analysis of social insects such as ants requires tracking many ants over time. This process is highly labor-intensive and tedious. Automatic tracking is challenging as ants often interact with one another, resulting in frequent occlusions that cause drifts in tracking. In addition, tracking many objects is computationally expensive. In this paper, we present a robust and efficient method for tracking multiple ants. We first prevent drifts by maximizing the coverage of foreground pixels at at global scale. Secondly, we improve speed by reducing markov chain length through dynamically changing the target proposal distribution for perturbed ant selection. Using a real dataset with ground truth, we demonstrate that our algorithm was able to improve the accuracy by 15% (resulting in 98% tracking accuracy) and the speed by 76%.
ISBN:1424494966
9781424494965
ISSN:1550-5790
2642-9381
DOI:10.1109/WACV.2011.5711555