Anomaly detection in video surveillance is a highly developed subject that is attracting
increased attention from the research community. There is great demand for intelligent …

As technology continues to develop, computer vision (CV) applications are becoming
increasingly widespread in the intelligent transportation systems (ITS) context. These …

We propose a network for Congested Scene Recognition called CSRNet to provide a data-
driven and deep learning method that can understand highly congested scenes and perform …

In this paper, we propose a novel encoder-decoder network, called extit {Scale Aggregation
Network (SANet)}, for accurate and efficient crowd counting. The encoder extracts multi …

We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++)
that contains “4,372” images with “1.51 million” annotations. In comparison to existing …

We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-
quality crowd density and count estimation by explicitly incorporating global and local …

Videos represent the primary source of information for surveillance applications. Video
material is often available in large quantities but in most cases it contains little or no …

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 …

Estimating crowd count in densely crowded scenes is an extremely challenging task due to
non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded …

The current concept of smart cities influences urban planners and researchers to provide
modern, secured and sustainable infrastructure and gives a decent quality of life to its …