Biolocomotion Detection in Videos
| dc.contributor.advisor | Wildes, Richard | |
| dc.contributor.author | Kang, Soo Min | |
| dc.date.accessioned | 2020-05-11T15:40:25Z | |
| dc.date.available | 2020-05-11T15:40:25Z | |
| dc.date.copyright | 2019-08 | |
| dc.date.issued | 2020-05-11 | |
| dc.date.updated | 2020-05-11T15:40:25Z | |
| dc.degree.discipline | Computer Science | |
| dc.degree.level | Doctoral | |
| dc.degree.name | PhD - Doctor of Philosophy | |
| dc.description.abstract | Animals locomote for various reasons: to search for food, to find suitable habitat, to pursue prey, to escape from predators, or to seek a mate. The grand scale of biodiversity contributes to the great locomotory design and mode diversity. In this dissertation, the locomotion of general biological species is referred to as biolocomotion. The goal of this dissertation is to develop a computational approach to detect biolocomotion in any unprocessed video. The ways biological entities locomote through an environment are extremely diverse. Various creatures make use of legs, wings, fins, and other means to move through the world. Significantly, the motion exhibited by the body parts to navigate through an environment can be modelled by a combination of an overall positional advance with an overlaid asymmetric oscillatory pattern, a distinctive signature that tends to be absent in non-biological objects in locomotion. In this dissertation, this key trait of positional advance with asymmetric oscillation along with differences in an object's common motion (extrinsic motion) and localized motion of its parts (intrinsic motion) is exploited to detect biolocomotion. In particular, a computational algorithm is developed to measure the presence of these traits in tracked objects to determine if they correspond to a biological entity in locomotion. An alternative algorithm, based on generic handcrafted features combined with learning is assembled out of components from allied areas of investigation, also is presented as a basis of comparison to the main proposed algorithm. A novel biolocomotion dataset encompassing a wide range of moving biological and non-biological objects in natural settings is provided. Additionally, biolocomotion annotations to an extant camouflage animals dataset also is provided. Quantitative results indicate that the proposed algorithm considerably outperforms the alternative approach, supporting the hypothesis that biolocomotion can be detected reliably based on its distinct signature of positional advance with asymmetric oscillation and extrinsic/intrinsic motion dissimilarity. | |
| dc.identifier.uri | https://hdl.handle.net/10315/37493 | |
| dc.language | en | |
| dc.rights | Author owns copyright, except where explicitly noted. Please contact the author directly with licensing requests. | |
| dc.subject | Computer science | |
| dc.subject.keywords | Biolocomotion | |
| dc.subject.keywords | Biolocomotion detection | |
| dc.subject.keywords | Biological motion | |
| dc.subject.keywords | Computer vision | |
| dc.subject.keywords | Videos | |
| dc.subject.keywords | Animacy detection | |
| dc.title | Biolocomotion Detection in Videos | |
| dc.type | Electronic Thesis or Dissertation |
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