Automated Tracking of Multiple C. Elegans

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

Wing and body motion during flight initiation in Drosophila revealed by automated visual tracking

The 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 attack and other parameters important to a mechanistic understanding of flapping flight. When compared with manual tracking methods, the algorithm estimates body position within 4.4±1.3% of the body length, while body orientation is measured within 6.5±1.9 deg. (roll), 3.2±1.3 deg. (pitch) and 3.4±1.6 deg. (yaw) on average across six videos. Similarly, stroke amplitude and deviation are estimated within 3.3 deg. and 2.1 deg., while angle of attack is typically measured within 8.8 deg. comparing against a human digitizer. Using our automated tracker, we analyzed a total of eight voluntary and two escape take-offs. These sequences show that Drosophila melanogaster do not utilize clap and fling during take-off and are able to modify their wing kinematics from one wingstroke to the next. Our approach should enable biomechanists and ethologists to process much larger datasets than possible at present and, therefore, accelerate insight into the mechanisms of free-flight maneuvers of flying insects

Automated visual tracking for studying the ontogeny of zebrafish swimming

The 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 fish's 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

Reliability Based Factors of Safety for VIV Fatigue Using NDP Riser High Mode VIV Tests

Understanding the level of conservatism in a riser system design for vortex-induced vibration (VIV) fatigue is an important issue for operators. This study represents a demonstration of the calibration methodology to derive consistent values for the Factor of Safety (FoS). The exercise is performed here based on medium scale VIV data and utilizing the most commonly used VIV prediction software by industry. The results emphasize the need for (i) a coherent approach to estimate the FoS to be used and (ii) monitoring/measurement of software improvements as this may increase risk of failure if the influence of such improvements on the FoS is not quantified.DeepStar (Consortium) (DeepStar Phase IX

Automated Visual Tracking for Behavioral Analysis of Biological Model Organisms

Capturing the detailed motion and behavior of biological organisms plays an important role in a wide variety of research disciplines. Many studies in biomechanics, neuroethology, and developmental biology rely on analysis of video sequences to understand the underlying behavior. However, the efficient and rapid quantification of these complex behavioral traits imposes a major bottleneck on the elucidation of many interesting scientific questions. The goal of this thesis is to develop a suite of model-based visual tracking algorithms that will apply across a variety of model organisms used in biology. These automated tracking algorithms operate in a high-throughput, high-resolution manner needed for a productive synthesis with modern genetic approaches. To this end, I demonstrate automated estimation of the detailed body posture of nematodes, zebrafish, and fruit flies from calibrated video. The current algorithm utilizes a generative geometric model to capture the organism's shape and appearance. To accurately predict the organism's motion between video frames, I incorporate a motion model that matches tracked motion patterns to patterns in a training set. This technique is invariant with respect to the organism's velocity and can easily incorporate training data from completely different motion patterns. The prediction of the motion model is refined using measurements from the image. In addition to high-contrast feature points, I introduce a region, segmentation model based on level sets that are formally integrated into the observation framework of an Iterated Kalman Filter (IKF). The prior knowledge provided by the geometric and motion models improves tracking accuracy in the presence of partial occlusions and misleading visual cues. The method is used to track the position and shape of multiple nematodes during mating behavior, zebrafish of different ages during escape response, and fruit flies during take off maneuvers. These applications demonstrate the modular design of this model-based visual tracking system, where the user can specify which components are appropriate to a given experiment. In contrast to other approaches, which are customized to a particular organism or experimental setup, my approach provides a foundation that requires little re-engineering whenever the experimental parameters are changed.</p