The Georgia Institute of Technology and Arizona State University are awarded grants to develop an integrated approach to automating measurements of insect behavior from video records. The study of insect behavior plays a fundamental role in biology, but progress is limited by the rate at which data can be gathered. Researchers have relied largely on direct observation or time-consuming manual annotation of video records. This project will create an automated solution that combines theory, algorithms, software modules, and databases of behavior measurements. These tools will be widely applicable to studies of animal behavior, but development will focus on the particularly rich and challenging problems offered by ants, where multiple interacting animals must be simultaneously tracked. Current multi-tracking technologies are limited in their ability to deal with the huge degree of target interaction in this context, including significant periods of occlusion of one target by another. This project will generate a novel approach that applies the graph-cut optimization method to video object segmentation. This method will be able to identify which portions of the video correspond to distinct targets even when they overlap. Accurate target segmentation will also facilitate more accurate adaptation to changes in appearance due to lighting or other environmental effects. The project will also develop novel behavior recognition methods that infer behavior from target configuration and appearance. Unlike traditional methods this approach will not rely on the state of the tracker and thus will avoid the compounding of recognition errors by tracking errors.
Two cross-cutting themes inform this project. The first is a focus on algorithms and methods compatible with modular software tools, thus allowing biologists to develop a customized solution to a wide range of sensing tasks. The second theme is the utilization of state-of-the-art ultra-high resolution imaging sensors to obtain more information about ant behavior and identity than is currently possible. These capabilities will enable insect biologists to frame and answer research questions that exceed the limited data collection capabilities of current methods. Algorithms and software modules will be widely disseminated, to maximize their power to transform biology in a more general setting. For more information visit the project website at www.kinetrack.org/
