Existing counting methods often adopt regression-based approaches and cannot precisely
localize the target objects, which hinders the further analysis (eg, high-level understanding …

We have created a large diverse set of cars from overhead images (Data sets, annotations,
networks and scripts are available from http://gdo-datasci. ucllnl. org/cowc/), which are useful …

Like computer vision before, remote sensing has been radically changed by the introduction
of deep learning and, more notably, Convolution Neural Networks. Land cover classification …

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a
myriad of cameras installed over a network. Injecting some level of automation could help …

Abstract We present the Caltech Fish Counting Dataset (CFC), a large-scale dataset for
detecting, tracking, and counting fish in sonar videos. We identify sonar videos as a rich …

Object counting is a hot topic in computer vision, which aims to estimate the number of
objects in a given image. However, most methods only count objects of a single category for …

Object counting, which aims to count the accurate number of object instances in images, has
been attracting more and more attention. However, challenges such as large-scale variation …

Vehicle detection is an important but challenging problem in Earth observation due to the
intricately small sizes and varied appearances of the objects of interest. In this paper, we use …

In this paper, we study the problem of object counting with incomplete annotations. Based
on the observation that in many object counting problems the target objects are normally …

While big cities are expected to exercise cost-effective, evidence-based planning, many are
under reactive management, facing simultaneous problems and limited resources. This …