On the Use of Efficient Projection Kernels for Motion-Based Visual Saliency Estimation
Abstract
In this paper, we investigate the potential of a family of efficient filters—the Gray-Code Kernels (GCKs)—for addressing visual saliency estimation with a focus on motion information. Our implementation relies on the use of 3D kernels applied to overlapping blocks of frames and is able to gather meaningful spatio-temporal information with a very light computation. We introduce an attention module that reasons the use of pooling strategies, combined in an unsupervised way to derive a saliency map highlighting the presence of motion in the scene. A coarse segmentation map can also be obtained. In the experimental analysis, we evaluate our method on publicly available datasets and show that it is able to effectively and efficiently identify the portion of the image where the motion is occurring, providing tolerance to a variety of scene conditions and complexities.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 10, 2022
- Source ID
- 10.3389/fcomp.2022.867289
Entities
People
- Elena Nicora
- Nicoletta Noceti
Organizations
- Air Force Office of Scientific Research