  In this work, we address a challenging problem of fine-grained and
coarse-grained recognition of object manipulation actions. Due to the
variations in geometrical and motion constraints, there are different
manipulations actions possible to perform different sets of actions with an
object. Also, there are subtle movements involved to complete most of object
manipulation actions. This makes the task of object manipulation action
recognition difficult with only just the motion information. We propose to use
grasp and motion-constraints information to recognise and understand action
intention with different objects. We also provide an extensive experimental
evaluation on the recent Yale Human Grasping dataset consisting of large set of
455 manipulation actions. The evaluation involves a) Different contemporary
multi-class classifiers, and binary classifiers with one-vs-one multi- class
voting scheme, b) Differential comparisons results based on subsets of
attributes involving information of grasp and motion-constraints, c)
Fine-grained and Coarse-grained object manipulation action recognition based on
fine-grained as well as coarse-grained grasp type information, and d)
Comparison between Instance level and Sequence level modeling of object
manipulation actions. Our results justifies the efficacy of grasp attributes
for the task of fine-grained and coarse-grained object manipulation action
recognition.
