Many videos depict people, and it is their interactions that inform us of their activities, relation
to one another and the cultural and social setting. With advances in human action …

We propose two complementary techniques to improve the performance of action
recognition systems. The first technique addresses the temporal interval ambiguity of actions …

In video-surveillance, violent event detection is of utmost interest. Although action
recognition has been well studied in computer vision, literature for violence detection in …

The objective of this work is to accurately and efficiently detect configurations of one or more
people in edited TV material. Such configurations often appear in standard arrangements …

This paper brings to light one of the most prominent applications of human activity
recognition which is the anomaly detection. Providing security to an individual is a major …

Spatial-temporal graph convolutional networks (ST-GCN) have achieved outstanding
performances on human action recognition, however, it might be less superior on a two …

Human action recognition is a way of retrieving videos emerged from Content Based Video
Retrieval (CBVR). It is a growing area of research in the field of computer vision nowadays …

We introduce a novel spatio-temporal deformable part model for offline detection of fine-
grained interactions in video. One novelty of the model is that part detectors model the …

In this work, we look into the problem of recognizing two-person interactions in videos. Our
method integrates multiple visual features in a weakly supervised manner by utilizing an …

We propose an approach for human interaction recognition (HIR) in videos using
multinomial kernel logistic regression with group-of-features relevance (GFR-MKLR). Our …