Crowd counting in single-view images has achieved outstanding performance on existing
counting datasets. However, single-view counting is not applicable to large and wide scenes …

Automatic estimation of the number of people in unconstrained crowded scenes is a
challenging task and one major difficulty stems from the huge scale variation of people. In …

In this paper, we propose two modified neural networks based on dual path multi-scale
fusion networks (SFANet) and SegNet for accurate and efficient crowd counting. Inspired by …

Crowd counting is a concerned yet challenging task in computer vision. The difficulty is
particularly pronounced by scale variations in crowd images. Most state-of-art approaches …

Crowd counting is the task of estimating people numbers in crowd images. Modern crowd
counting methods employ deep neural networks to estimate crowd counts via crowd density …

Crowd counting from unconstrained scene images is a crucial task in many real-world
applications like urban surveillance and management, but it is greatly challenged by the …

Crowd counting has recently attracted increasing interest in computer vision but remains a
challenging problem. In this paper, we propose a trellis encoder-decoder network (TEDnet) …

To fully leverage the data captured from different scenes with different view angles while
reducing the annotation cost, this paper studies a novel crowd counting setting, ie only using …

Crowd counting has been widely studied by computer vision community in recent years. Due
to the large scale variation, it remains to be a challenging task. Previous methods adopt …

In this paper, we address the task of object counting in images. We follow modern learning
approaches in which a density map is estimated directly from the input image. We employ …