Visual Tracking: From An Individual To Groups Of Animals

This thesis is concerned with the development and application of visual tracking techniques to the domain of animal monitoring. The development and evaluation of a system which uses image analysis to control the robotic placement of a sensor on the back of a feeding pig is presented first. This single-target monitoring application is then followed by the evaluation of suitable techniques for tracking groups of animals, of which the most suitable existing technique is found to be a Markov chain Monte Carlo particle filtering algorithm with a Markov random field motion prior (MCMC MRF, Khan et al. 2004). Finally, a new tracking technique is developed which uses social motion information present in groups of social targets to guide the tracking. This is used in the new Motion Parameter Sharing (MPS) algorithm. MPS is designed to improve the tracking of groups of targets with coordinated motion by incorporating motion information from targets that have been moving in a similar way. Situations where coordinated motion information should improve tracking include animal flocking, people moving as a group or any situation where some targets are moving in a correlated fashion. This new method is tested on a variety of real and artificial data sequences, and its performance compared to that of the MCMC MRF algorithm. The new MPS algorithm is found to outperform the MCMC MRF algorithm during a number of different types of sequences (including during occlusion events and noisy sequences) where correlated motion is present between targets. This improvement is apparent both in the accuracy of target location and robustness of tracking, the latter of which is greatly improved

Visual Tracking: From An Individual To Groups Of Animals

This thesis is concerned with the development and application of visual tracking techniques to the domain of animal monitoring. The development and evaluation of a system which uses image analysis to control the robotic placement of a sensor on the back of a feeding pig is presented first. This single-target monitoring application is then followed by the evaluation of suitable techniques for tracking groups of animals, of which the most suitable existing technique is found to be a Markov chain Monte Carlo particle filtering algorithm with a Markov random field motion prior (MCMC MRF, Khan et al. 2004). Finally, a new tracking technique is developed which uses social motion information present in groups of social targets to guide the tracking. This is used in the new Motion Parameter Sharing (MPS) algorithm. MPS is designed to improve the tracking of groups of targets with coordinated motion by incorporating motion information from targets that have been moving in a similar way. Situations where coordinated motion information should improve tracking include animal flocking, people moving as a group or any situation where some targets are moving in a correlated fashion. This new method is tested on a variety of real and artificial data sequences, and its performance compared to that of the MCMC MRF algorithm. The new MPS algorithm is found to outperform the MCMC MRF algorithm during a number of different types of sequences (including during occlusion events and noisy sequences) where correlated motion is present between targets. This improvement is apparent both in the accuracy of target location and robustness of tracking, the latter of which is greatly improved

Detecting lameness in livestock using ’resampling condensation’ and ’multi-stream cyclic hidden Markov models

A system for the tracking and classification of livestock movements is presented. The combined ‘tracker-classifier ’ scheme is based on a variant of Isard and Blakes ‘Condensation ’ algorithm [6] known as ‘Re-sampling Condensation’ in which a second set of samples is taken from each image in the input sequence based on the results of the initialCondensation sampling. This is analogous to a single iteration of a genetic algorithm and serves to incorporate image information in sample location. Re-sampling Condensationrelies on the variation withinthe spatial (shape) model being separated into pseudo-independent components (analogous to genes). In the system a hierarchical spatial model based on a variant of the Point DistributionModel [16] is used to model shape variation accurately. Results are presented that show this algorithm gives improved tracking performance, with no computational overhead, over Condensation alone. Separate Cyclic Hidden Markov Models are used to model ‘Healthy ’ and ‘Lame ’ movements withinthe Condensationframework in a competitive manner such that the model best representing the data will be propagated through the image sequence.

Iowa State University, Courses and Programs Catalog 2014–2015

The Iowa State University Catalog is a one-year publication which lists all academic policies, and procedures. The catalog also includes the following: information for fees; curriculum requirements; first-year courses of study for over 100 undergraduate majors; course descriptions for nearly 5000 undergraduate and graduate courses; and a listing of faculty members at Iowa State University.https://lib.dr.iastate.edu/catalog/1025/thumbnail.jp