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.
Visual multi-object tracking has the potential to accelerate many forms of quantitative analyses, especially in research communities investigating the motion, behavior, or social interactions within groups of animals. Despite its potential for increasing analysis throughput, complications related to accessibility, adaptability, accuracy, or scalable application arise with existing tracking systems. Several iterations of prototyping and testing have led us to a multiobject tracking system -ABCTracker -that is: accessible in both system as well as technical knowledge requirements, easily adaptable to new videos, and capable of producing accurate tracking data through a mixture of automatic and semi-automatic tracking features.
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