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Facial Expression Recognition with Hybrid Models

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Segmentation, Object detection, tracking, foundation models

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Smart Event Detection for Highlight Clips

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+ Have you ever missed a highlight during a match? This system can capture highlights based on a user prompt or directly from a video. + It uses advanced, state-of-the-art approaches—such as Meta’s SAM3—to track objects, detect events, and generate short highlight clips. +

+ The goal is to combine modern segmentation models (such as SAM) with classical computer vision techniques. Segmentation serves as a strong perception layer, while event detection is driven by motion-based features such as trajectories, velocity, and frequency analysis, along with lightweight reasoning. + The system follows a modular design, consisting of a general perception and feature extraction pipeline combined with task-specific event detection modules. +

+ The system is primarily designed for human action detection (e.g., waving, raising a hand, standing up). As an extension, it can also handle simple sports scenarios, such as tracking a ball moving toward or crossing a goal, demonstrating its ability to generalize to multi-object interactions. + +

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Facial Expression Recognition with Hybrid Models

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