A hybrid social influence model (HSIM) has been proposed which is a novel and automatic method for pedestrian motion segmentation. One of the major attractions of the HSIM is its capability to handle motion segmentation ...

We propose a novel Gaussian kernel based integration model (GKIM) for anomalous entities detection and localization in pedestrian flows. The GKIM integrates spatio-temporal features for efficient and robust motion ...

We propose density independent hydrodynamics model (DIHM) which is a novel and automatic method for coherency detection in crowded scenes. One of the major advantages of the DIHM is its capability to handle changing density ...

Surveillance systems regularly create massive video data in the modern technological era, making their analysis challenging for security specialists. Finding anomalous activities manually in these enormous video recordings ...

Facial emotion recognition is a crucial task for human-computer interaction, autonomous vehicles, and a multitude of multimedia applications. In this paper, we propose a modular framework for human facial emotions’ ...

Human action recognition is still a challenging problem and researchers are focusing to investigate this problem using different techniques. We propose a robust approach for human action recognition. This is achieved by ...

Betydelige fremskritt har blitt gjort innen identifikasjon av individuelle husdyr ved bruk av konvolusjonelle nevrale nettverk (CNNs). Til tross for disse fremskrittene, er det fortsatt rom for å forbedre deres ytelse. ...

Camera trap imaging has emerged as a valuable tool for modern wildlife surveillance, enabling researchers to monitor and study wild animals and their behaviors. However, a significant challenge in camera trap data analysis ...

The recognition of different activities in sports has gained attention in recent years for its applications in various athletic events, including soccer and cricket. Cricket, in particular, presents a challenging task for ...