Crowd anomaly detection is a key research area in vision-based surveillance. Most of the
crowd anomaly detection algorithms are either too slow, bulky, or power-hungry to be …

In this paper, we propose a novel method for unusual human activity detection in crowded
scenes. Specifically, rather than detecting or segmenting humans, we devised an efficient …

Significant progress has been made in terms of computational models of bottom-up visual
attention (saliency). However, efficient ways of comparing these models for still images …

It is common for CCTV operators to overlook interesting events taking place within the crowd
due to large number of people in the crowded scene (ie marathon, rally). Thus, there is a …

The purpose of this paper is to demonstrate a perceptually based spatio-temporal
computational framework for visual saliency estimation. We have developed a new spatio …

We present a novel approach for spatiotemporal saliency detection by optimizing a unified
criterion of color contrast, motion contrast, appearance, and background cues. To this end …

A semi-supervised, one-dimensional recurrent neural network (RNN) approach called RVS
has been proposed in this paper for video salient object detection. The proposed RVS …

Human attention is highly influenced by multi-modal combinations of perceived sensory
information and especially audiovisual information. Although systematic behavioral …

The last decades have seen great progress in saliency prediction, with the success of deep
neural networks that are able to encode high-level semantics. Yet, while humans have the …

Visual Saliency of a video sequence can be computed by combining spatial and temporal
features that attract a user's attention to a group of pixels. We present a method that …