Detect What You Can: Detecting and Representing Objects Using Holistic Models and Body Parts
University of California, Los Angeles · Stanford University · +1 more institution
Abstract
Detecting objects becomes difficult when we need to deal with large shape deformation, occlusion and low resolution. We propose a novel approach to i) handle large deformations and partial occlusions in animals (as examples of highly deformable objects), ii) describe them in terms of body parts, and iii) detect them when their body parts are hard to detect (e.g., animals depicted at low resolution). We represent the holistic object and body parts separately and use a fully connected model to arrange templates for the holistic object and body parts. Our model automatically decouples the holistic object or body parts from the model when they are hard to detect. This enables us to represent a large number of…
Citation impact
- FWCI
- 20.27
- Percentile
- 100%
- References
- 41
Authors
6Topics & keywords
- Pascal (unit)
- Torso
- Computer science
- Artificial intelligence
- Computer vision
- Object (grammar)
- Low resolution
- Representation (politics)