A new convolutional neural network (CNN) architecture for 2D driver/passenger pose estimation and seat belt detection is proposed in this paper. The new architecture is more nimble and thus more suitable for in-vehicle monitoring tasks compared to other generic pose estimation algorithms. The new architecture, named NADS-Net, utilizes the feature pyramid network (FPN) backbone with multiple detection heads to achieve the optimal performance for driver/passenger state detection tasks. The new architecture is validated on a new data set containing video clips of 100 drivers in 50 driving sessions that are collected for this study. The detection performance is analyzed under different demographic, appearance, and illumination conditions. The results presented in this paper may provide meaningful insights for the autonomous driving research community and automotive industry for future algorithm development and data collection.

________________________________ Robert Cook ________________________________ Jean-François Charles ii ACKNOWLEDGEMENTS I would like to express my gratitude toward the University of Iowa, School of Music, and explicitly David Gompper for trusting, supporting and leading me to be where I am now. This would not have been possible without help of all of the other faculty members at the School of Music. I am also very grateful to my mother for teaching me how to be strong while sensitive, and to my father for encouraging me to earn what I deserve through hard work.iii ABSTRACT Recently, there have been efforts to design more efficient ways to internalize music by applying the disciplines of cognition, psychology, temporality, aesthetics, and philosophy.Bringing together the fields of art and science, computational techniques can also be applied to musical analysis. Although a wide range of research projects have been conducted, the automatization of music analysis remains emergent. Importantly, patterns are revealed by using automated tools to analyze core musical elements created from melodies, harmonies, and rhythms, high-level features that are perceivable by the human ear. For music to be captured and successfully analyzed by a computer, however, one needs to extract certain information found in the lower-level features of amplitude, frequency, and duration.Moreover, while the identity of harmonic progressions, melodic contour, musical patterns, and pitch quantification are crucial factors in traditional music analysis, these alone are not exclusive. Visual representations are useful tools that reflect form and structure of nonconventional musical repertoire.Because I regard the fluidity of music and visual shape as strongly interactive, the ultimate goal of this thesis is to construct a practical tool that prepares the visual material used for musical composition. By utilizing concepts of time, computation, and composition, this tool effectively integrates computer science, signal processing, and music perception. This will be obtained by presenting two concepts, one abstract and one mathematical, that will provide materials leading to the original composition. To extract the desired visualization, I propose a fully automated tool for musical analysis that is grounded in both the mid-level elements of loudness, density, and range, and low-level features of frequency and duration. As evidenced by my sinfonietta, Equilibrium, this tool, capable of rapidly analyzing a variety of musical examples such as instrumental repertoire, electro-acoustic music, improvisation and folk music, is highly beneficial to my proposed compositional procedure.2 Kris Shaffer, "Computational Musicology," Push Pull Fork, last modified January 2016, http://pushpullfork.com/2016/01/computational-musicology/ 3 Recursive or repetitive decision trees that are used for modeling conditions in which events are repeated over time, or for modeling predictable events that occur over time. 4 A contiguous sequence of n items from a given sequence of...

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