SUMMARYThe fruit fly Drosophila melanogaster is a widely used model organism in studies of genetics, developmental biology and biomechanics. One limitation for exploiting Drosophila as a model system for behavioral neurobiology is that measuring body kinematics during behavior is labor intensive and subjective. In order to quantify flight kinematics during different types of maneuvers, we have developed a visual tracking system that estimates the posture of the fly from multiple calibrated cameras. An accurate geometric fly model is designed using unit quaternions to capture complex body and wing rotations, which are automatically fitted to the images in each time frame. Our approach works across a range of flight behaviors, while also being robust to common environmental clutter. The tracking system is used in this paper to compare wing and body motion during both voluntary and escape take-offs. Using our automated algorithms, we are able to measure stroke amplitude, geometric angle of Supplementary material available online at

SUMMARYThe zebrafish Danio rerio is a widely used model organism in studies of genetics, developmental biology, and recently, biomechanics. In order to quantify changes in swimming during all stages of development, we have developed a visual tracking system that estimates the posture of fish. Our current approach assumes planar motion of the fish, given image sequences taken from a top view. An accurate geometric fish model is automatically designed and fit to the images at each time frame. Our approach works across a range of fish shapes and sizes and is therefore well suited for studying the ontogeny of fish swimming, while also being robust to common environmental occlusions. Our current analysis focuses on measuring the influence of vertebra development on the swimming capabilities of zebrafish. We examine wild-type zebrafish and mutants with stiff vertebrae (stocksteif ) and quantify their body kinematics as a function of their development from larvae to adult (mutants made available by the Hubrecht laboratory, The Netherlands). By tracking the fish, we are able to measure the curvature and net acceleration along the body that result from the fishʼs body wave. Here, we demonstrate the capabilities of the tracking system for the escape response of wild-type zebrafish and stocksteif mutant zebrafish. The response was filmed with a digital high-speed camera at 1500·frames·s -1 . Our approach enables biomechanists and ethologists to process much larger datasets than possible at present. Our automated tracking scheme can therefore accelerate insight in the swimming behavior of many species of (developing) fish. Supplementary material available online at

Future underwater vehicles will be increasingly called upon to work in cluttered environments and to interact with their surroundings. These vehicles will need sensors that work efficiently at short range and be highly maneuverable at low speed. To obtain insights into principles and mechanisms of low-speed operation in cluttered environments, we examine a fish that excels in this regime, the black ghost knifefish Apteronotus albifrons. This fish hunts in dark or turbid water using a short-range self-generated electric field to sense its surroundings. Coupled with this unique mode of sensing is an unusual ribbon fin propulsion system that confers high multidirectional maneuverability at low speeds. To better understand the relationship between body morphology and common maneuvers of this fish, we utilized an idealized ellipsoidal body model, Kirchhoff's equations, and an optimal control algorithm for generating trajectories. We present evidence that common fish trajectories are optimal, and that these trajectories complement the sensory abilities of the fish. We also discuss prototypes of the sensing and propulsion systems of the fish with a view to providing alternative approaches for underwater vehicle design where high maneuverability in geometrically complex environments is needed.

This paper presents a method for model based automated tracking of multiple worm-like creatures. These methods are essential for accurate quantitative analysis into the genetic basis of behavior that involve more than one organism. An accurate worm model is designed using the geometry of planar curves and nonlinear estimation of the model's parameters are performed using a central difference Kalman filter (CDKF). The filter can naturally be expanded to estimate the locations of multiple worms and determine when they are occluding each other. The predicted location of the models at each iteration allows for an efficient method to determine the regions that are undergoing occlusions. Experiments on actual C. Elegans mating sequence data demonstrate the quality of the proposed method.

Abstract-This paper presents a method for model based automated tracking of multiple worm-like creatures. These methods are essential for accurate quantitative analysis into the genetic basis of behavior that involve more than one organism. An accurate worm model is designed using the geometry of planar curves and nonlinear estimation of the model's parameters are performed using a central difference Kalman filter (CDKF). The filter can naturally be expanded to estimate the locations of multiple worms and determine when they are occluding each other. The predicted location of the models at each iteration allows for an efficient method to determine the regions that are undergoing occlusions. Experiments on actual C. Elegans mating sequence data demonstrate the quality of the proposed method.