  Animals locomote for various reasons: to search for food, find suitable
habitat, pursue prey, escape from predators, or seek a mate. The grand scale of
biodiversity contributes to the great locomotory design and mode diversity.
Various creatures make use of legs, wings, fins and other means to move through
the world. In this report, we refer to the locomotion of general biological
species as biolocomotion. We present a computational approach to detect
biolocomotion in unprocessed video.
  Significantly, the motion exhibited by the body parts of a biological entity
to navigate through an environment can be modeled 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. We exploit 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) to detect
biolocomotion. An 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 features
combined with learning is assembled out of components from allied areas of
investigation, also is presented as a basis of comparison.
  A novel biolocomotion dataset encompassing a wide range of moving biological
and non-biological objects in natural settings is provided. Also, biolocomotion
annotations to an extant camouflage animals dataset are 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.
