Browsing by Subject "generative AI"

Now showing 1 - 3 of 3
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    Crime Detection from Pre-crime Video Analysis
    (2024-05) Kilic, Sedat; Tuceryan, Mihran; Zheng, Jiang Yu; Tsechpenakis, Gavriil; Durresi, Arjan
    This research investigates the detection of pre-crime events, specifically targeting behaviors indicative of shoplifting, through the advanced analysis of CCTV video data. The study introduces an innovative approach that leverages augmented human pose and emotion information within individual frames, combined with the extraction of activity information across subsequent frames, to enhance the identification of potential shoplifting actions before they occur. Utilizing a diverse set of models including 3D Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and a specially developed transformer architecture, the research systematically explores the impact of integrating additional contextual information into video analysis. By augmenting frame-level video data with detailed pose and emotion insights, and focusing on the temporal dynamics between frames, our methodology aims to capture the nuanced behavioral patterns that precede shoplifting events. The comprehensive experimental evaluation of our models across different configurations reveals a significant improvement in the accuracy of pre-crime detection. The findings underscore the crucial role of combining visual features with augmented data and the importance of analyzing activity patterns over time for a deeper understanding of pre-shoplifting behaviors. The study’s contributions are multifaceted, including a detailed examination of pre-crime frames, strategic augmentation of video data with added contextual information, the creation of a novel transformer architecture customized for pre-crime analysis, and an extensive evaluation of various computational models to improve predictive accuracy.
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    Exploring outlooks towards generative AI-based assistive technologies for people with Autism
    (ACM, 2023-04-28) Giri, Deepak; Brady, Erin
    The last few years have significantly increased global interest in generative artificial intelligence. Deepfakes, which are synthetically created videos, emerged as an application of generative artificial intelligence. Fake news and pornographic content have been the two most prevalent negative use cases of deepfakes in the digital ecosystem. Deepfakes have some advantageous applications that experts in the subject have thought of in the areas of filmmaking, teaching, etc. Research on the potential of deepfakes among people with disabilities is, however, scarce or nonexistent. This workshop paper explores the potential of deepfakes as an assistive technology. We examined Reddit conversations regarding Nvdia’s new videoconferencing feature which allows participants to maintain eye contact during online meetings. Through manual web scraping and qualitative coding, we found 162 relevant comments discussing the relevance and appropriateness of the technology for people with Autism. The themes identified from the qualitative codes indicate a number of concerns for technology among the autistic community. We suggest that developing generative AI-based assistive solutions will have ramifications for human-computer interaction (HCI), and present open questions that should be investigated further in this space.
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    Leveling the Playing Field: Generative A.I. & Writing Anxiety among Graduate Students​
    (2025-04-03) Piper, Gemmicka; Ameen, Mahasin; Lowe, M. Sara