Efficient Training of Multiple Ant Tracking

Abstract

The applicability of automated motion analysis is immense and continues to grow as our ability to record objects of interest becomes easier and less expensive. In the case of multi-object tracking, data association methods have been proposed to improve handling of occlusions. These methods are strongly affected by their ability to measure association affinities between fragmented object trajectories. Obtaining labeled training examples for learning how to measure these associations can be expensive and time-consuming. We propose an interactive training framework that utilizes an uncertainty based active sampling approach in combination with semi-supervised learning in order to reduce the number of labeled examples needed for training. Additionally, an affinity scoring function is learned with Random Forest to speed up learning affinity measures in order to make the interactive training framework possible. Experimental results on two 10,000 frame video sequences of ant colonies demonstrates a significant reduction in the amount of labeled examples needed over random sampling.

Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence

“…To train our classifiers, we need a sufficiently large, manually labeled dataset. Rice et al ( 2015 ) proposed a method to create a similar dataset interactively, reducing the required manual work. Also, the circular coding scheme of our markers causes some bit configurations to appear similar under certain object poses.…”