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ISEP - DM – Engenharia de Inteligência Artificial

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http://hdl.handle.net/10400.22/18077

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Browsing ISEP - DM – Engenharia de Inteligência Artificial by Sustainable Development Goals (SDG) "04:Educação de Qualidade"

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  • Melhorar a deteção de anomalias em video com deteção de objeto
    Publication . PEREIRA, BRUNO ALVES; Soares, Pedro Miguel Machado
    Video Anomaly Detection (VAD) is a critical task in video surveillance and security systems, aiming to automatically identify events that deviate from normal patterns. These systems enable real-time monitoring, offer scalability for processing large volumes of data across diverse environments, and help reduce human error. Despite recent advances, most VAD models rely solely on spatio-temporal features. This project investigates the impact of incorporating contextual information, specifically object-level features, into the pipeline of a State of The Art (SoTA) VAD model. For this aim, we propose modifications in a SoTA model by presenting a new architecture that integrates object detection features. Intermediate and late fusion techniques were explored to determine the most effective method for combining object-level with spatio-temporal features used by the model. The experiments were conducted on a modified version of a SoTA dataset, adapted for weakly supervised training. The findings indicate that integrating object-level features enhances the performance of the baseline model, with improvements observed across three key metrics: Area Under the Curve (AUC), Average Precision (AP), and F1-score, particularly in the late fusion models. Freezing weights of the base model was shown essential to achieve the best results. However, the inclusion of the new channel introduced additional computational costs during training and a slight increase in inference time. Although these factors can affect the scalability of the project, they are not very significant since tasks can be parallelized, or executed in better hardware infrastructures. This work demonstrates that incorporating contextual cues from object detection into existing VAD frameworks can lead to better anomaly discrimination, paving the way for more reliable and context-aware surveillance systems.
    2025-07-14Master thesis Embargoed access Show more