We discuss an attentional model for simultaneous object tracking and recognition that is
driven by gaze data. Motivated by theories of perception, the model consists of two …

We formulate tracking as an online decision-making process, where a tracking agent must
follow an object despite ambiguous image frames and a limited computational budget …

Deep learning architectures are an extremely powerful tool for recognizing and classifying
images. However, they require supervised learning and normally work on vectors of the size …

Deep learning architectures are an extremely powerful tool for recognizing and classifying
images. However, they require supervised learning and normally work on vectors the size of …

Every day millions and millions of surveillance cameras monitor the world, recording and
collecting huge amount of data. The collected data can be extremely useful: from the …

Every day millions and millions of surveillance cameras monitor the world, recording and
collecting huge amount of data. The collected data can be extremely useful: from the …

Cameras can naturally capture sequences of images, or videos, and for computers to
understand videos, they must track to connect the past with the present. We focus on two …

Humans have the ability to view a scene and form an overall representation in a remarkably
short length of time. However, due to the complexity of visual search, it is reasonable to …

Multi-stage visual architectures have recently found success in achieving high classification
accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by …

This project extends the attentional tracking model developed by Bazzani et al.[1] to include
gaze selection strategies which operate in the presence of partial information and on a …