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 …

Movement dataset reviews exist but are limited in coverage, both in terms of size and
research discipline. While topic-specific reviews clearly have their merit, it is critical to have a …

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 …

Detecting coherent groups is fundamentally important for crowd behavior analysis. In the
past few decades, plenty of works have been conducted on this topic, but most of them have …

Crowd counting presents enormous challenges in the form of large variation in scales within
images and across the dataset. These issues are further exacerbated in highly congested …

Crowd counting and crowd density estimation methods are of great significance in the field
of public security. Estimating crowd density and counting from single image or video frame …

Crowded scene understanding is a fundamental problem in computer vision. In this study,
we develop a multi-task deep model to jointly learn and combine appearance and motion …

Object detection in videos has drawn increasing attention recently with the introduction of
the large-scale ImageNet VID dataset. Different from object detection in static images …

In this work, we propose a novel crowd counting network that progressively generates crowd
density maps via residual error estimation. The proposed method uses VGG16 as the …

Yapay sinir ağları ve makine öğrenmesi, uzun yıllardır birçok problemin çözümünde
kullanılmıştır. Problemlerin ve modellerin karmaşıklaşması ve veri sayısındaki artış …