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.
The important aspects of our work include: a) A compact representation of the grasp and motion-constraints using some popular and some contemporary schemes. b) Demonstrating the usefulness of information from coarse-grained and fine-grained grasp attributes as well as motion-constraints for fine-grained and coarse-grained action recognition. c) A differential experimental analysis involving subsets of grasp and motion-constraints features, to provide more insights on the usefulness of grasp information alone, motion-constraints information alone, and grasp and motion-constraints based information together for intended classification problem. d) Comparisons between Instance and Sequence level modeling of object manipulation actions using fine-grained grasp information. e) An extensive experimental evaluation using different contemporary multi-class and binary classifiers (with a multi-class voting strategy), which also serves as a useful comparative study of popular classifiers for the manipulation action recognition problem. This analysis also helps to demonstrate that different classification frameworks, largely arrive at a consensus with respect to our hypothesis about using grasp and motion-constraints for fine-grained action classification. We demonstrate our results on a large Yale Human Grasping dataset @cite_29 which involves various tasks on different objects.
@cite_29
2170243694
We analyze functional manipulations of handheld objects, formalizing the problem as one of fine-grained grasp classification. To do so, we make use of a recently developed fine-grained taxonomy of human-object grasps. We introduce a large dataset of 12000 RGB-D images covering 71 everyday grasps in natural interactions. Our dataset is different from past work (typically addressed from a robotics perspective) in terms of its scale, diversity, and combination of RGB and depth data. From a computer-vision perspective, our dataset allows for exploration of contact and force prediction (crucial concepts in functional grasp analysis) from perceptual cues. We present extensive experimental results with state-of-the-art baselines, illustrating the role of segmentation, object context, and 3D-understanding in functional grasp analysis. We demonstrate a near 2X improvement over prior work and a naive deep baseline, while pointing out important directions for improvement.