1,187 research outputs found
MoWLD: a robust motion image descriptor for violence detection
© 2015, Springer Science+Business Media New York. Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bag-of-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the non-parametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts
MoWLD: A Robust Motion Image Descriptor for Violence Detection
Abstract Automatic violence detection from video is a hot topic for many video surveillance applications. However, there has been little success in designing an algorithm that can detect violence in surveillance videos with high performance. Existing methods typically apply the Bagof-Words (BoW) model on local spatiotemporal descriptors. However, traditional spatiotemporal features are not discriminative enough, and also the BoW model roughly assigns each feature vector to only one visual word and therefore ignores the spatial relationships among the features. To tackle these problems, in this paper we propose a novel Motion Weber Local Descriptor (MoWLD) in the spirit of the well-known WLD and make it a powerful and robust descriptor for motion images. We extend the WLD spatial descriptions by adding a temporal component to the appearance descriptor, which implicitly captures local motion information as well as low-level image appear information. To eliminate redundant and irrelevant features, the nonparametric Kernel Density Estimation (KDE) is employed on the MoWLD descriptor. In order to obtain more discriminative features, we adopt the sparse coding and max pooling scheme to further process the selected MoWLDs. Experimental results on three benchmark datasets have demonstrated the superiority of the proposed approach over the state-of-the-arts
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
TikTok Cyberbully Responses: Communicating the Narrative
This study analyzed TikTok cyberbullying response videos to understand how content creators use verbal and nonverbal communication within their performance, creating audience engagement and awareness for cyberbullying and social advocacy issues. This study was conducted with 200 TikTok creator accounts analyzing communication, performance, engagement, and analytics narrative content analysis. Codes were separated into themes that represented the creator\u27s emotional responses. These responses were analyzed to learn how the audience presented the content. The combination of narrative content analysis and analytics data was used to determine the effectiveness of the creators\u27 ability to engage with their audience and create cyberbullying and social advocacy awareness. The study found a connection between a TikTok content creator’s performance and audience engagement. The content creators with successful performances convinced their audience to engage with comments supporting anti-cyberbullying and promoting social causes. The combination of scholarly research and new media technology allows this topic to be examined through a Communications lens analyzing verbal and nonverbal communication. The research project can be helpful for academics, social advocacy groups, and those interested in TikTok content engagement
Abstracts of Papers, 89th Annual Meeting of the Virginia Academy of Science, May 25-27, 2011, University of Richmond, Richmond VA
Full abstracts of the 89th Annual Meeting of the Virginia Academy of Science, May 25-27, 2011, University of Richmond, Richmond V
Affective Communication for Socially Assistive Robots (SARs) for Children with Autism Spectrum Disorder: A Systematic Review
Research on affective communication for socially assistive robots has been conducted to
enable physical robots to perceive, express, and respond emotionally. However, the use of affective
computing in social robots has been limited, especially when social robots are designed for children,
and especially those with autism spectrum disorder (ASD). Social robots are based on cognitiveaffective models, which allow them to communicate with people following social behaviors and
rules. However, interactions between a child and a robot may change or be different compared to
those with an adult or when the child has an emotional deficit. In this study, we systematically
reviewed studies related to computational models of emotions for children with ASD. We used the
Scopus, WoS, Springer, and IEEE-Xplore databases to answer different research questions related to
the definition, interaction, and design of computational models supported by theoretical psychology
approaches from 1997 to 2021. Our review found 46 articles; not all the studies considered children
or those with ASD.This research was funded by VRIEA-PUCV, grant number 039.358/202
- …