Video surveillance systems are popular and used in public places such as market places,
shop** malls, hospitals, banks, streets, education institutions, city administrative offices …

Automated scene analysis has been a topic of great interest in computer vision and
cognitive science. Recently, with the growth of crowd phenomena in the real world, crowded …

Convolutional Neural Networks (CNNs) have been applied to visual tracking with
demonstrated success in recent years. Most CNN-based trackers utilize hierarchical features …

Due to the advancement of camera hardware and machine learning techniques, video
object tracking for surveillance has received noticeable attention from the computer vision …

Video anomaly detection and localization is still a challenging task in the computer vision
field. Previous methods took this task as an outlier detection problem, which computed the …

Rapid population aging and advances in sensing technologies motivate the development of
unobtrusive healthcare systems, designed to unobtrusively collect an elderly's personalized …

The proliferation of multimedia devices over the Internet of Things (IoT) generates an
unprecedented amount of data. Consequently, the world has stepped into the era of big …

Anomaly detection in videos is still a challenging task among the computer vision
community. In this paper, an efficient anomaly detection method based on stacked sparse …

Owing to the massive amount of video data being generated as a result of high proliferation
of surveillance cameras, the manpower to monitor such system is relatively expensive …

We present a trajectory extraction and behavior-learning algorithm for data-driven crowd
simulation. Our formulation is based on incrementally learning pedestrian motion models …