Background subtraction, although being a very well-established field, has required
significant research efforts to tackle unsolved challenges and to accelerate the progress …

Although the traits emerged in a mass gathering are often non-deliberative, the act of mass
impulse may lead to irrevocable crowd disasters. The two-fold increase of carnage in crowd …

In this work, we present a novel background subtraction from video sequences algorithm
that uses a deep Convolutional Neural Network (CNN) to perform the segmentation. With …

Automated crowd density estimation and counting are popular and important topic in crowd
analysis. The last decades witnessed different of many significant publications in this field …

The way people walk is a strong correlate of their identity. Several studies have shown that
both humans and machines can recognize individuals just by their gait, given that proper …

In this work, we present a novel background subtraction system that uses a deep
Convolutional Neural Network (CNN) to perform the segmentation. With this approach …

Crowd analysis is very important for human behavior analysis, safety science, computational
simulation, and computer vision applications. One of the most popular applications is video …

Existing multi target multi camera tracking (MTMCT) datasets are small in terms of the
number of identities and video lengths. The creation of new real world datasets is hard as …

Using multiple moving cameras with different and time-varying views can significantly
expand the capability of multiple human tracking in larger areas and with various …

A new method (HoGG) for human detection based on Gabor filters and Histograms of
Oriented Gradients is presented in this paper. The effect of Gabor preprocessing is analyzed …