Estimating count and density maps from crowd images has a wide range of applications
such as video surveillance, traffic monitoring, public safety and urban planning. In addition …

Pedestrian detection is a key problem in computer vision, with several applications that have
the potential to positively impact quality of life. In recent years, the number of approaches to …

State-of-the-art methods for counting people in crowded scenes rely on deep networks to
estimate crowd density. They typically use the same filters over the whole image or over …

We propose a novel crowd counting model that maps a given crowd scene to its density.
Crowd analysis is compounded by myriad of factors like inter-occlusion between people due …

Pedestrian detection in crowded scenes is a challenging problem since the pedestrians
often gather together and occlude each other. In this paper, we propose a new occlusion …

This paper aims to develop a method that can accurately estimate the crowd count from an
individual image with arbitrary crowd density and arbitrary perspective. To this end, we have …

Crowd counting or density estimation is a challenging task in computer vision due to large
scale variations, perspective distortions and serious occlusions, etc. Existing methods …

Cross-scene crowd counting is a challenging task where no laborious data annotation is
required for counting people in new target surveillance crowd scenes unseen in the training …

We introduce a detection framework for dense crowd counting and eliminate the need for the
prevalent density regression paradigm. Typical counting models predict crowd density for an …

Recent advances in pedestrian detection are attained by transferring the learned features of
Convolutional Neural Network (ConvNet) to pedestrians. This ConvNet is typically pre …